prefrontal cortex short-term potentiation model for working memory

Posted comment on ´A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation` by F. Fiebig and A. Lansner and published in Journal of Neuroscience 37(1) p. 83:


Researchers hypothesize that the encoding and maintenance of informational items in working memory requires persistent elevated activity of neural networks in the prefrontal cortex (PFC). The models involve short-term, non-associative synaptic plasticity (non-Hebbian) producing an active buffer with periodic reactivations refreshing the decaying synaptic firing hence, retaining the memory. However, the models cannot explain encoding of novel associations since this type of learning, according to Fiebig and Lansner is presynaptic, which implies all the outgoing synapses of the active neuron are enhanced. The authors of this article, Fiebig and Lansner, re-examined experiments where there are single informational units during delay periods and showed that synaptic activity of the prefrontal cortex area was instead more variable. They found discrete gamma bursts of activity associated with the information of multiple items in working memory. As a result, Fiebig and Lansner suggested a recently identified fast-expressing form of Hebbian synaptic plasticity (called associative Short-term Potentiation – STP) was involved. STP occurs after brief high frequency bursts and decays not with time, but in an activity dependent manner. Therefore, the authors suggested that memory reactivation occurs in discrete oscillatory bursts rather than with sustained neuronal activity.

For their experiments on the cortex, Fiebig and Lansner used a NEST and a computational model of the cortical layer 2/3 network consisting of 16 hyper-columns (HC) with 5760 pyramid cells and 384 inhibitory basket cells. Each HC had 24 basket cells and the pyramid cells were divided into 12 functional multi-columns (MC) of 30 pyramidal cells each. The HCs were laid out on a hexagonal grid. Fiebig and Lansner computed the axonal delay between the presynaptic neuron (termed i) and post-synaptic (termed j) based on known conduction speeds and distances between the MCs. The pyramidal cells of the HC exhibit lateral AMPA mediated excitatory projections to the basket cells with a calculated connection probability which they designated ppb. The pyramidal cells received inhibitory feedback via the GABA mechanism from basket cells with a connection probability defined as pBp. The pyramidal cells also formed NMDA and AMPA connections within the HCs and across them with a connection probability termed ppp. The entire model was described by the authors as demonstrating appropriate plastic neuronal connections and featured a total of over 13 million synapses between pyramidal cells, over 100,000 excitatory connections from pyramidal cells to basket cells in their respective HCs as well as many inhibitory connections. Fiebig and Lansner used a modified AdEX IAF neuron model with a spike frequency adaptation. This was compatible with a custom made BCPNN synapse model in NEST through the addition of the intrinsic excitability current they designated Ibetaj. AMPA and NMDA synapses were modeled with a spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule so that presynaptic and postsynaptic rates could be calculated as well as co-activation.

Memory performance was assessed by cued and free recall word tests. For the latter, pattern activation was counted and for the former, half of each cued pattern was counted and then checked to see whether the pattern was fully activated afterwards. The authors also performed behavioural studies of 2 types: Study A used the Betula Study of a large battery of cognitive tests including study and immediate free recall of a word list; and in Study B the cued recall used was the word recall study of Gershberg and Shimamura (1994).

In the case of the test of single item memory encoding and performance of the free recall task (termed Demonstration 1) and carried out using a delayed match to sample task (delayed free recall with one item), Fiebig and Lansner found that the ground state for the first second was characterised by low-rate (1.7HZ),  irregular, asynchronous firing of pyramidal cells whereas local basket cells were found to often spike together even if not synchronizing the firing activity globally. Targeted stimulation from 1-2 secs onwards of one MC in each HC led to increases of firing in the stimulated pyramidal population which led to rapid bursting of the local basket cells. These then inhibited all the neurons in their HC resulting in lower firing of non-specific cells. The authors interpreted this as the firing network counterbalancing the increased activity in some MCs by decreasing the firing of neighbouring populations. Approx. 1 sec after the beginning of the stimulation brief spontaneous reactivations of the originally stimulated MCs began.  These reactivations were found to be gamma oscillation bursts and synchronization of the firing cells was attributed to fast feedback inhibition and short connection delays in the excitatory associative connections. The specific firing rate of the pattern activated cells was approx. 25Hz and stable for the duration of reactivation. After 120msecs, the evoked firing was terminated due to synaptic depression, but spontaneous reactivations were still observed resulting in a pattern of repeated spontaneous attractor reactivations in discrete oscillatory bursts similar to Lundqvist`s  view, but based on Hebbian STP as a result of the new learning. The non-stimulated pyramidal population showed decreased firing rate both during and after stimulus.

Fiebig and Lansner also performed experiments to show how the network learned and could simultaneously store larger numbers of items indicative of multiple item working memory. This list learning without intermittent replay task was designated Demonstration 2. In the first 20 secs the network ground state consisted of background plus evoked irregular spiking of pyramidal cells and triggering of basket cells. The network was then stimulated with 12 patterns of 1 second each with intervals of 500msecs. Unspecific background activity decreased as a result of competing neural event such as the working memory task with distractor task and attention diverted to abolish active maintenance. The authors found that as learning progressed, the network encoded the properties of the structured input. A linear relationship between how recently a pattern had been trained and the excitability of the relevant pyramidal member neurons was found since recently active neurons featured a less negative bias current than neurons that had been silent for a longer time. Memory performance during the free recall phase was measured by tracking the autonomous attractor reactivations and Fiebig and Lansner found that reactivations of recently trained attractors dominated (although only the last 5 patterns) whereas earlier patterns were reactivated by cued recall.

In Demonstration 3, Fiebig and Lansner examined the PSPs of successful attractor memory activation. They found that patterns were excited to spike at 40HZ via one of 3 separate scenarios: one type of presynaptic neuron in the same MC; a presynaptic neuron in another MC; or a basket cell. They averaged several hundred recorded post-synaptic traces to give isolated PSP test results and found that synapses may depress so the peri-stimulus PSP magnitude depended on the duration of the 40 Hz input (25 ms interspike interval). At the ground state near −67 mV, EPSP amplitudes were initially large, but quickly depressed. Strong inhibition incurred from presynaptic basket cells and these were found not to depress.

Fiebig and Lansner also performed two Simulation studies. In Simulation Study 1, multi-item working memory was tested using list learning with intermittent replay. Twelve items were presented one every two seconds, but the background activity rate was not manipulated by the authors so reactivation could occur during the inter-stimulus interval. Therefore, early patterns were observed and could strengthen during the intermittent reactivations occurring in the learning period. This was attributed to ´memory refresh` or short-term memory consolidation. Free recall testing produced greater results with 5-8 patterns recalled and it was found that primacy and recency rules applied. The authors found that the mean firing rates of patterns eventually recalled increased from 1.3HZ at baseline to 2.7HZ after learning. In Simulation Study 2, cued recall in word list learning was examined. Here, Fiebig and Lansner found a recency effect. However, the weak middle position that exhibited a free recall probability of only 20-30% could be improved by cueing recall. Recall success then rose to 80%.

In summary, Fiebig and Lansner in their experiments supported  a functional cortical working memory model where there is fast Hebbian synaptic plasticity of pyramidal cells (STP) representing the stimulated cells with reactivations of a gamma oscillation burst nature accompanied by fast, basket cell mediated feedback inhibition. Hence, it appears that the encoding, maintenance and reactivation of working memory information occurs from limited sustained activity and then discrete oscillatory bursts rather than persistent activity of the relevant cells. The authors were able to expand their work by showing that verbal memory learning and recall could be shown by their model and was limited to 5-8 patterns recalled. An increase in frequency from 1.3HZ to 2.7HZ was shown with this learning. Primacy and recency effects were also demonstrated in both free and cued recall. Therefore, the authors concluded that fast Hebbian short term potentiation is a key mechanism in working memory.


What makes this article interesting is that it reaffirms the view that brain waves can indicate the type of functioning that a brain area is carrying out at the immediate time, or the state of the connectivity between areas at that time. Brain waves are seen because of a measurable level of synchronized firing of neurons and the changes in them with sleep states has been known for a long time. Later research has been associating the different types generally with different cognitive functions in the awake state, eg. thinking with beta oscillations, meditation with alpha, and even on a more smaller scale, synchronised waves of firing of particular cells within an area, eg. the theta clock-spiking cells of the hippocampus described in a previous post (March 2017 – Zhang and colleagues).

The article described in this post involved the authors looking at brain waves associated with what they described as working memory (WM). They concluded that working memory requires fast Hebbian synaptic plasticity of pyramidal cells (STP) of cortical layer 2/3 from stimulated cells with reactivations of a gamma oscillation burst nature accompanied by fast, basket cell mediated feedback inhibition. In this way, encoding, maintenance and reactivation of working memory information appeared to occur from limited sustained activity and then discrete oscillatory bursts rather than persistent activity of the relevant cells. However, my view is that the activation observed in their experiments cannot be described as working memory in its true sense, but can be instead attributed to non-complex learning and recall of information. This is because no manipulation of inputted information or recalled information was required by the nature of the experiments used. The experiments used instead demanded that word information was learnt and recalled exactly as learnt independent of whether the experiment was structured so that recall was free or cued. Therefore, the memory mechanisms required for such a task would be visual information input, learning (the necessary sensory store formation, short-term store formation and long-term store formation) and recall without processing (Salt, 2011) ie. neuronal firing of cell assemblies representing the coded information. Since manipulation of neither inputted material or recalled material was required, then the working memory capability could not in my view be said to be involved.

However, even if working memory was not involved, the experiments do show the brain wave patterns of the cortical layer 2/3 cells during the learning and recall stages of verbal lists (the basis of Fiebig and Lansner`s experiments). Such cognitive requirements demand the cooperation and connectivity of multiple brain areas and mechanisms. In the case of the information input, both visual and attentional pathways are activated and neuronal cell assemblies are built at the appropriate cortical area at the highest hierarchical level in order to represent electrically the stimulus. Connectivity between firing neurons and the patterns formed continue until one of three things happen: the stimulus is stopped (stimulus interval) as would be the case if a stimulus is continually or repetitively presented; the external stimulus is stopped, but firing is continued by internally induced means involving the PFC and/or hippocampus (theoretical, but observed in some cases for both areas); or the normal biochemical firing limits of the cell are reached (cell refractory period) and firing ceases until the cell biochemically recovers. The presentation of another word is unlikely to produce the same neuronal cell assembly and therefore, firing of a new grouping begins and continues in the same vein as the first. According to the authors of the article, firing of the ´old` assembly also continues intermittently as shown by the bursts of activity observed. This is what the authors say provide conditions that shift the electrical representation from sensory store/short term memory to long-term memory and not persistent cell firing as previously thought. This corresponds to the theoretical Option 2 described above where the external stimulus is stopped (the stimulus is now the new one ie. the new word being presented), but firing is continued by internally induced means involving the PFC and/or hippocampus. Biochemically, whether Option 1 or 2 occurs, means that there is a shift from short to long term memory and the short term physiological changes seen in each firing neuronal cell, eg. the rise in internal calcium ion concentration, change in pyruvate kinase activity cause long-term changes, eg. gene transcription alterations, higher receptor number. The overall effect is that the firing propensity of those cells within that group (ie. the cells of that group are more sensitive to firing if the same input is given) is increased and this represents the Hebbian plasticity cited by the authors.  Firing of areas is inhibited by GABAergic excitation as seen in other cortical and hippocampal systems.

As indicated above, in my view, intermittent firing could be explained by the firing of the entorhinal cortex and certain areas of the hippocampus (DG dentate gyrus leading to CA1, CA3) which can occur independently of external stimulus and possibly create the conditions required to shift the short-term memory formation to long-term memory formation in the absence of real-time stimulus repetition. Firing of PFC cells independent of external stimulus is also shown to occur and this would link in with its working memory and visuomotor functions for example.

The second part of Fiebig and Lansner`s experiments and the proof of successful learning is the recall of the word when asked. Recall can be free or cued. Free recall means that firing of the neuronal cell assembly representing one word will prompt the firing of another and this is brought about by the temporal connections (although in this case strictly speaking order) between one functional cell assembly and the next. We know ourselves that associating one word with another for whatever reason will increase the chance of remembering. Cued recall can be easier and hence, recall performance as the authors showed is greater because a characteristic of the event is used to prompt the firing of the whole assembly and perhaps others through association.

Fiebig and Lansner in their article link the firing of neuronal cell assemblies in the PFC with working memory performance although their experiments looked at cortical layer 2/3 firing with no definitive brain area given and working memory may not be strictly what was being observed. The cortical cells used could have come from other brain areas associated with the cognitive functions required, eg. post-parietal cortex known for item maintenance and manipulation of information, visuomotor areas. If samples were from the PFC then firing could be attributed to this area`s attentional function rather than working memory since PFC pyramidal cells are said to be multi-tasking (working memory and attention – work by Messenger). The authors looked at the firing of cell assemblies representing the words and found columnar cell assemblies consisting of excitatory pyramidal cells and inhibitory cells stimulated by GABA. Since they thought that long-term potentiation (LTP), which is the signal of Hebbian plasticity, requires a longer time and working memory is fast they interpreted their findings as LTP translating as long-term storage as consistent with biochemical explanation and working memory, the sensory store or short-term memory version. Fiebig and Lansner suggested that different early forms of LTP such as STP (short-term potentiation – un-stimulated, no presynaptic spikes) and E-LTP (NMDA dependent, independent of protein synthesis, possible presynaptic transmitter release, AMPA R phosphorylation by CaM-CaMKII receptor insertions, potassium from postsynaptic NMDA R activation as retrograde messenger for presynaptic STP induction) were responsible. In this case it is likely that E-LTP represents these early changes observed in the neuron as a result of firing continuously after neurotransmitter release and stimulation of the post-synaptic membrane. STP may represent the later stages as gene transcription occurs before the  post-synaptic changes in AMPA R number takes place traditionally associated with LTP. This is maybe why it needs no presynaptic stimulation since gene transcription changes have already been instigated.

Therefore, the biochemical changes taking place during the learning and recall processes have been explained, but Fiebig and Lansner used brain waves to demonstrate the functioning of the neurons. They found a ground state which consisted of low rate (1.7HZ), irregular firing of the pyramid cells plus basket cells of the chosen columns firing in synchronous bursts that were not global. One to two seconds after the presentation of the stimulus the authors found an increase in the population of stimulated pyramidal cells as expected and rapid bursting of basket cells which lead to the inhibition of the firing of the cells. Bursts of gamma wave reactivation occurred. After 120 seconds then there was a decrease in evoked stimulation due to neural firing depression because of the cellular refractory periods. Other researchers support the observation of bursts of gamma activity associated with working memory and that persistent activity is not necessary for short-term retention, but a general increase in overall activity for neurons fired in the working memory pattern.  This is different to other views where persistent activity is required for single item delayed match to sample tasks. The authors suggested that working memory manifests as multiple forms of firing activity eg bursting, persistent, fast neural sequences akin to synchronous firing chains and phase relationships.

In order to interpret the brain wave patterns observed by Fiebig and Lansner in their experiments, we have to look at the type of brain waves observed and their hypothesized associated biochemical function. The lowest frequency (1/2 to 3Hz), highest amplitude wave of synchronized neural activity is designated the delta wave and this is seen in Stage 3 (or 4 depending on how the stages are defined) of sleep. This stage of sleep (deep, delta or slow wave sleep – SWS) is characterized by its low frequency (0.5-4HZ) waves with large amplitude and appearance of some sleep spindles. It represents coordination of interregional cortical communication and acetylcholine receptors appear to be important. Sleep-active neurons located in the ventrolateral preoptic nucleus (VLPO) play a crucial role in the induction and maintenance (glucose effect on VLPO) of this stage and also are important in memory formation. During slow wave sleep, both GABAergic neurons of the nucleus reticularis thalami (NRT) and thalamocortical (TC) neurons discharge high-frequency bursts of action potentials brought about by low-threshold calcium spikes due to T-type Ca2+ channel activation. Sharp-wave/ripple (SPW/R) complexes, which are short episodes of increased activity with superimposed high-frequency oscillations of the hippocampus are also involved in this stage plus the neuronal replay of previous behaviour. In my view, the delta waves observed in this sleep state could represent the firing of the GABAergic neurons mediating inhibition of excitation whereas the sharp -wave/ripple (SPW/R) complexes observed in the hippocampus could be the equivalent of the gamma waves seen in the awake state and representing the neuronal cell assembly connectivity.

In contrast to the delta state, theta brain waves with a frequency of 4-7HZ can exist as ´background` activity or as bursts such as the theta clock-spiking cell activity. Theta wave activity is found in the PFC, sensory cortex, hippocampus and cingulate cortex and appears to be needed for a wide range of cognitive functions, eg. memory, perception, consciousness, working memory with high frequency stimulation, recall (in mPFC and EC), learning (in EC and hippocampus), visual memory (in V4 visual cortex and PFC), spatial memory (in hippocampus), and temporal coordination (in PFC). It is this last function which in my opinion holds the key to the overlying purpose of the theta brain oscillation, ie. that it demonstrates temporal coordination including order. This is achieved through bursts of activity when observed in areas dominated by other firing eg. alpha or beta waves, or when constant, eg. as in the case of the theta clock-spiking cells. The mechanism causing this type of intermittent firing could be via GABAergic interneurons in the hippocampus which switch off firing in connected areas, hence giving a timing to firing in a manner similar to Morse code, ie. dot (firing) dot dash (not firing/switched off firing). Evidence for this view comes from spatial navigation, the existence of hippocampus and PFC coupling, the observation that it is at its highest amplitude in the DG, and that its presence in the cingulate cortex reflects the amount of irrelevant information perceived in an event. The view is also supported by the appearance of theta brain waves during the sleep stages. Theta brain waves arise in Stage 2 as unsynchronized beta and gamma waves reduce, and as conscious awareness fades. The stage includes the occasional sleep spindle (8-14Hz) generated by the thalamic pacemaker which lasts half a second plus high amplitude K complex waves of the theta type with short negative high voltage peaks, followed by a slower positive complex and then a final negative peak with each complex lasting 1-2 mins. These serve to protect sleep and suppress responses to outside stimuli as well as to aid in sleep-based memory consolidation and information processing.   Theta waves represent the deep sleep of the NREM  and is shown by synchronous activity in the thalamocortical network (thalamus to M1, SMA, PFC, V1, auditory cortex), modulated by inhibitory inputs from the thalamic reticular nucleus (TRN) which is itself modulated by GABAergic neurons from the lateral hypothalamus. This provides support for the view that theta waves portray background activity as visual stimuli are gone and memory consolidation and information processing occurs through timing and GABA interneuron functioning. The question as to why the neurons are better serviced by burst activity and not continuous activity can be answered by looking at the biochemical mechanisms involved. Continuous activity would result in the area suffering ´electrical shut-down` due to the cells experiencing simultaneous absolute refractory periods and therefore, burst activity is a natural switching off, but not to this point. This could mean that the firing activity of the neuronal cell assembly is stronger since timed together through phase locking and more capable of firing more frequently than if each cell is activated to biochemical ´exhaustion`.

The alpha brain wave (8-12HZ) occurs in the quiet, waking state. It is found when the subject is relaxed, during meditation, fantasizing, daydreaming, learning (alpha activity is said to occur for pre-stimulus activity – Myers) and with spontaneous firing which can be alpha or beta in nature. It is observed in the occipital, parietal, and anterior areas and is responsible for item maintenance (post parietal and occipital), episodic memory recall of visual information and in recall theta-alpha (4-13HZ) oscillations are said to bind the hippocampus, PFC and striatum. It is this latter function that may indicate in my view the importance of alpha brain waves. A demonstrable connectivity between these areas may allow information to be linked together through simultaneous firing of neuronal cells within a group and associated with other groups (perhaps of different areas) within a definite time period. This view is supported by: the role of alpha waves in daydreaming/fantasizing (internal recall without stimulus from outside), learning (internal repetition – explains observations of gamma waves for cell assembly building with alpha waves); appearance at the end of Stage 1 of sleep as the individual relaxes; the observation that alpha activity inhibits disruptive distracting information in recall; and alpha amplitude varies with memory maintenance and updating demands (increased alpha post cue is associated with high relevant load).

The brain wave oscillation with higher frequency than alpha is the beta wave with a frequency of 13-25HZ and appears when awake in general thinking, analysis, talking and learning and specifically episodic memory recall of visual information, attention, motor control, perception, and awareness. It is observed in all activated cortical areas, the sub-thalamus nucleus, basal ganglia, hippocampus and motor cortex. This type of brain wave like alpha in my view represents the active firing of neurons of the cortex representing memory recall, working memory, attention and perception. This also explains its appearance in Stage 5 of REM sleep and importance to procedural memory and learning. Here it is likely to be involved in the strengthening of the neuronal assemblies by recall of features whilst in the dream-state.

Of all the brain wave types, the most interesting is probably the gamma type which often exists as 30-80 HZ bursts. These are observed in nearly all brain areas including sensorimotor, frontoparietal, PFC and mPFC (visuomotor), parietal cortex and cingulate cortex and are linked to neuronal connections and groupings, perception, attention, memory (long-term and short-term), awareness, visuomotor associations, and spatial memory (firing patterns during wakefulness correlate greatly to spatial patterns observed in sleep during gamma wave activity phases). In my view, the gamma wave activity is linked to the phase of interneuron activity and is responsible for the firing of cell group assemblies. Gamma bursts likely represent the connectivity between the PFC and other areas such as visuomotor areas, or PFC and hippocampus. In the latter, it exists with theta connectivity in spatial memory between ventral/medial PFC areas and the hippocampus. In this case too, a functionally columnar network of recurrently connected excitatory and inhibitory neural populations is seen. Firing of the hippocampal and PFC cells link objects together as required for spatial memory (eg. object and location). Gamma oscillations could also represent the links between different areas of the PFC a view supported by Brovelli who showed that performance of visuomotor associations was characterized by an increase in gamma power oscillations and functional connectivity over the sensorimotor and frontoparietal network, in addition to medial prefrontal areas. The superior parietal area plays a driving role in the network, exerting Granger causality on the dorsal premotor area. Premotor areas act as a relay from the parietal to medial prefrontal cortices, which plays a receiving role in the network. Further analysis shows that visuomotor mappings reflect the coordination of multiple subnetworks with strong overlapping firing of motor and frontoparietal areas. In the case, of working memory, gamma bursts are observed in the post-parietal cortex responsible for the manipulation of material plus item maintenance and the lateral occipital cortex responsible for item maintenance. Different areas of the PFC are involved in different types of WM as shown by Pasternak (work on senses) and Soto (work on TMS).

Therefore, from the above explanation of brain waves and how they can change with area and function we can see that Fiebig and Lansner looked at pyramidal and basket cells from the cortical layer 2/3 of an indefined area, but undergoing a specific task, eg.  learning and recall. Their observation of the theta waves is justified by their suggested role as ´background` activity or as bursts such as the theta clock-spiking cell activity and their involvement in perception, awareness, working memory, recall (in mPFC and EC), learning (in EC and hippocampus), visual memory (in V4 and PFC), spatial memory (in hippocampus), and temporal coordination (in PFC). It is likely that in the experiments carried out here the theta oscillations as bursts provide the temporal coordination required for the input, storage and recall of the word information with the intermittent firing via the inhibitory GABAergic interneurons of the cellular columns and possibly through hippocampus and PFC connectivity. It is likely that the authors would have seen lots of other areas working during the course of the experiment in seeing a word, learning a word, recalling a word and would have seen other brain waves in play if examined.

Therefore, what makes this article interesting is the insight it gives us on the mechanisms in play during learning and recall for one particular area and specific cell types. These mechanisms are not limited to the biochemical exchanges of ions or the transmission of chemical signals for example, but include how these biochemical actions manifest as measurable synchronized cell assemblies and cognitive function. What is also clear is that fine analysis of experimental results is required and that we cannot assume that the mechanisms and neuronal connectivity patterns that occur in one area can be given as doctrine for others. As always believed nature is complex and the understanding of the brain, a master of changeability and adaptability, requires a flexible, exacting scientific approach if it is to be ever completely grasped.

Since we`re talking about the topic …………

……working memory training programs are said to increase working memory performance. (They could mean attentional system positive differences, but still an overall increase in working memory performance is observed). Therefore, can we assume that priming or training programs will not affect the timing and systems of firing seen by the authors in a repeat of these experiments, but may affect the strength of firing since increased myelination and increased attention are said to be the beneficial results of working memory training?

……age differences have been reported for learning and recall linked to the increased amount of irrelevant information input and learning associated with rising age. Can we assume that older age groups exhibit the same patterns of brain wave activity shown by Fiebig and Lansner, or does age have an effect on the levels, or timing of the gamma bursts? Administration of a drug that temporarily prevents the opening of potassium channels in the PFC leads to the restoration of working memory performance associated with age. Would changes in gamma activity mirror the administration?



Posted in brain waves, neuronal firing, short-term potentiation, Uncategorized, working memory | Tagged , , ,

action of anaesthetic agents at the neuronal cell membrane

Posted comment on ´When the lights go out` by Philip Ball and published in New Scientist no. 3115 4th March 2017 p. 36


As Ball states in the beginning of his article, after 177 years of using anaesthetic agents for surgery we are still not exactly sure on the biochemical mechanisms behind them apart from that their administration affects particular molecules in the brain and this results in the subject losing consciousness. What is known, with knowledge coming from the fields of biochemistry and biophysics, is that the initial stages of the agents` effects involve them binding to specific ´receptor` molecules in a traditional ´lock and key` binding type mechanism. However, there is a wide range of agents of various molecular sizes that can do this, a concept not completely in keeping with the ´lock and key` mechanism. Ball gives as an example, xenon, which is a fairly common anaesthetic agent. Xenon gas exists as lone unreactive atoms, not compatible with water molecules and preferring negative and positively charged particles, but favouring a non-polar environment which of course is part of the basic structure of neuronal cell membranes. This preference, discovered over a century ago, is a common feature of many anaesthetic agents and it was hypothesized early on that they might bind to lipids in the cell membranes, accumulate and make the cell swell or distort by disrupting the adjacent channels which allow ions to pass through the membrane during normal nerve transmission. As expected, the result is that the cell`s capability to transmit a neuronal signal is disrupted.

This neuronal model was expanded by Cantor in 1997 who hypothesized that binding of the anaesthetic agents to the neuronal membrane molecules was not ´indiscriminate`, but that they bound to molecules associated around the ion channels. This would influence how they clustered together and could cause changes to the curvature of the membrane itself. The hypothesis at the time lacked detail and it was believed that the reported changes would be too small to make a difference to the nerve signal. However, later research has shown that this may be an incorrect assumption since indications are that small changes in membrane structure may have more significant effects on membrane function than expected. Ball reports in his article the views of Machta and Veatch who worked to the physics concept that at a critical point or critical temperature a system can undergo an abrupt change in state. Machta, Veatch and also Sethna believe that binding of the anaesthetic agents may affect the ´critical temperature` of the cell membrane making the cell more sensitive to slight changes (the so-called Meyer-Overton rule). The researchers suggested that close to a critical temperature the neuronal cell membrane molecules are constantly rearranging themselves and that in the membrane there are ´rafts` of regularly packed molecules (mostly cholesterol and saturated fats) that drift within a more disorderly matrix of unsaturated fats. Channels and receptors function when surrounded by appropriate molecules and within appropriate membrane ´rafts`. Machta, Veatch and also Sethna suggested that the anaesthetic agents bind to the membrane molecules and consequently alter the temperature at which the membrane critical state is achieved. This results in the channels not attaining their functioning status. Machta and Veatch`s hypothesis was supported by their own experiments using alcohols such as ethanol that act as anaesthetic agents. These substances were shown to lower the critical temperature of the membrane, ie. it would have to be colder before the appropriate rafts form for correct channel functioning. They also used as evidence the case of two lipid-loving drugs that should act as anaesthetics, but do not and found that these failed to alter the membrane`s critical temperature. The reverse was also found. Compounds that raised the critical temperature counteracted the effects of the anaesthetic agents eg. hexadecanol suppressed the effect of ethanol in tadpoles.  The theory appears to apply to both intravenously delivered anaesthetics as well as inhaled, small molecule anaesthetics according to Forman.

However, there are other hypotheses on how anaesthetic agents work and Ball in his article discusses the quantum-physics based view of Luca Turin. Turin suggests that some general anaesthetics cause electrons of membrane bound molecules to jump from one to another, thus altering the molecule`s functioning. His hypothesis was based on ideas about how the sense of smell works. He proposed that scent is perceived not from the shapes and binding of specific molecules (the biochemical view), but by the vibrations the molecules cause and these influence electrons jumping across gaps in the olfactory receptors. In the case of the anaesthetic agent, xenon, Turin proposed that xenon inserts itself directly into the membrane molecules and influences signaling by providing new, energetically favourable pathways within the molecule along which individual electrons could jump. Such electron currents would produce changes in the spin of the molecules, a property which can be measured. Turin and colleagues produced evidence for the hypothesis in the form of fruit flies which exhibit increases in electron spin when exposed to anaesthetic agents such as xenon, nitrous oxide and chloroform. The researchers also expanded the hypothesis by suggesting that the sites of the anaesthetic agents were molecules of the mitochondrial membrane. Forman, however, warned against the acceptance of what he called ´zombie theories` (ie. where experiments cannot show definitive evidence, but neither can the idea be definitively dismissed) and therefore to date, common acceptance of the action of anaesthetic agents is through binding of the agents to cellular membrane molecules related to ion channel functioning.

Ball concludes his article by expounding the importance of knowing the mechanism behind anaesthetic agents linking it to the design of better agents with fewer side effects and higher efficiency so that surgical doses can be lowered.


What makes this article interesting is that it describes something that gives measurable results, but involves mechanisms that range from the common and provable (e.g. blood concentration, lock and key binding, changes in intracellular ion concentrations) to the more esoteric, unprovable (eg. lipid rafts, electron transfer, consciousness.) Even the question ´Can we use the knowledge about natural sleep, which also involves loss of consciousness to explain the action of anaesthetic agents and vice versa?` is interesting because although sleep is widely researched, it too is not definitively explained in terms of neurochemistry. Hence, it is shown again that we need new ideas, new experimental techniques, and actually probably new physical theories. We cannot assume that the physics of biological materials is the same as the physics of metals, air, stone, and fluids for example since even if we take the example of the brain neuron, we have multiple physical states existing within millimetres of each other, eg we have solid objects, fluids, gaps, all within close quarters and these can experience microchanges in milliseconds within molecules or affecting the outside of molecules. To solve the mechanisms of anaesthetic agents we need to grasp what is going on at this level and because the brain consists of more than one cell, we also need to understand how what happens to one cell relates to a group as the neurochemical changes here relate to consciousness. By studying both sleep and anaesthesia perhaps the biochemical mechanisms of neurons and consciousness may be elucidated.

Ball`s article describes the knowledge associated with anaesthetic agents at the membrane microscale. However, to compare the two, we should probably look first at their effect at the macroscale and compare it to what happens in sleep. It is clear that anaesthesia and sleep differ in their instigation. Surgical anaesthesia normally involves the subject being given a cocktail of drugs applied over different times eg. a sedative (or other anaesthetic agent), a compound to paralyse the muscles and a pain perception blocker. The drugs can be given internally or locally, but this article because of this blog`s emphasis on the brain will consider only those anaesthetic substances given internally and having an effect on the brain. The anaesthetic agents are transported within the blood system and cross the blood-brain barrier to act at specific sites in the brain. However, the instigators of sleep, are molecules internally produced eg. melatonin increases for sleep, increased cortisol for wakefulness, and there are links to adenosine levels (eg. a drop leads to sleep), although there may be circadian rhythms of the levels of these molecules and reactions to external environmental factors such as light and darkness.

Whether sleep or anaesthetic agent the ultimate result is the loss of consciousness and this follows after certain brain areas are affected. In the case of sleep, the brain areas believed to be affected were thought previously to be the hypothalamus and reticular activating system in the brainstem, but now a network of structures is thought to be involved. Recent research has shown that the ventrolateral preoptic nucleus (VLPN) of the hypothalamus appears to be a switch between wakefulness and sleep and output from here during sleep inhibits activity in the brain stem, but maintains stimulation of the cerebral cortex either directly or indirectly. Circadian rhythms appear to be the work of activity of the suprachiasmatic nucleus (internal clock) and pineal gland (melatonin production). Studies on the action of anaesthetics have indicated that the VLPN is also stimulated through the activity of alpha2 adrenergic receptors (although mainly GABA receptors are present in this area) as well as activity in the thalamus, cerebral cortex and brain stem. Therefore, there is similarity in the areas affected by both sleep and anaesthesia.

In general the areas affected by anaesthesia, ie. the brain stem, cerebral cortex and thalamus show functional and effective connectivity which varies depending on the agent used, its dose, and network affected. A study on the anaesthetic, propofol (work by Chennu) showed that increasing the dosage administered to a group of 20 individuals meant that some subjects were still conscious at the maximum dose and were still able to do the given task as demonstrated by EEG readings of alpha brain waves. The experimenters found that even before the test started that some subjects were more susceptible than others to the propofol given and exhibited higher brain activity at the baseline than those less susceptible. This was correlated to exhibited delta-alpha brain wave coupling. Studies by Hudatz on decreases in the global cerebral metabolic rate and blood flow found that the thalamus was a common site of modulation by several anaesthetic agents, but the effect may be secondary to effects on the cerebral cortex. Using fMRI, Bukhari hypothesized that anaesthetic agents demonstrated specific signatures of brain functional networks and interactions, eg. medetomidine exhibited different functional connectivity to isoflurane, propofol and urethane (perhaps a sign of different levels of sodium ion channels or GABA receptors in these areas?). The Default Mode network-thalamic network and lateral cortical network-thalamic network was affected by medetomidine (influences alpha 2 adrenergic receptors) exhibiting a sedative function and vasoconstriction whereas these areas were not affected by isoflurane (demonstrates GABA activity, is an anaesthetic and vasodilator). Cortical-thalamic interaction was found to be modulated by the type and depth of anaesthesia and therefore, it was concluded that it is important to study anaesthesia function in networks rather than in single brain areas.

This is a reasonable assumption especially when we consider the Global Workplace Theory of consciousness where conscious awareness is achieved when there is global connectivity of participating neurons in particular brain areas. Consciousness is linked to firing in the areas of the cingulate cortex, parietal areas, prefrontal cortex, and temporal areas such as amygdala, hypothalamus and insular cortex. The application of anaesthetic agents results in a loss of neuronal firing and action potentials which manifests as dampened stimulation, a disruption of higher order cortical information integration and connectivity and loss of consciousness. This change in activity and connectivity can be observed with monitoring brain waves.

In the case of natural sleep, there is distinctive brain wave pattern activity (amplitudes and frequencies) in the 4 stages of NREM sleep and the REM stage. It is a not clear cut with bursts of particular kinds of activity eg sleep spindle occurring in certain stages. The progress of anaesthetic agent administration can also be observed through brain wave changes with more of the brain going to slow wave oscillations (SWS – slow frequency, high amplitude) typical of NREM stages 1-3 of sleep as loss of awareness proceeds. The SWS oscillations are regulated by the thalamus and the action of thalamic type (T-type) calcium ion channels. Studies on brain waves after the administration of the anaesthetic agent, etomidate, shows decreased 1-4HZ brain waves (theta) observed in wakefulness and increased alpha brain wave oscillations (8-12HZ) and beta (12-30HZ) (in fact, paradoxical since beta waves are actually linked to excitation) and increased sleep spindles (NREM stage 2 of normal sleep). The brain wave activity is said not to be linked to GABA R binding in the thalamus (Mesbata).

Studies on another anaesthetic agent inducing loss of consciousness, the substance profolol, shows that brain wave activity is not simple. After the induction phase, the surgical phase is actually maintained by a combination of different drugs and this produces different brain wave patterns in the different phases. In Phase 1, where there is a light state of general anesthesia, a decrease in beta brain wave activity (13 to 30 Hz) is observed as well as an increase in alpha activity (8 to 12 Hz) and delta activity (0 to 4 Hz). In Phase 2 the beta activity decreases and alpha and delta activity increases and brain wave activity resembles that seen in NREM stage 3 sleep. Phase 3 is defined as a deeper state of anaesthesia and the EEG activity exhibits flat periods interspersed with periods of alpha and beta activity (burst suppression) with time between the alpha bursts lengthening as the anaesthetic state deepens.  Surgery is actually carried out in Phase 2 and 3. In the final phase, Phase 4, EEG is completely flat (isoelectric). The REM stage of normal sleep involves acetylcholine firing and a highly active cortex. However, it is believed that in the case of anaesthesia, activation occurs through the GABA mediated inhibition of striato-thalamic pathways and in fact, direct injection of acetylcholine in thalamus has been shown to overcome anaesthesia.

Therefore, we have shown that anaesthesia and sleep although both cause loss of consciousness, do not share exactly the same brain wave activity patterns. These differences may be due to dissimilarity in the physiological structure of the brain areas involved and may indicate the different molecular mechanisms in play between sleep and anaesthesia. The action of neurotransmitters is one such factor. In the case of sleep, many different neurotransmitters play a role. For example: histamine demonstrates decreased activity during the NREM stages of sleep and exhibits the lowest levels in REM, but is at a high level in wakefulness; 5HT occurs in the awake state and decreases in the REM stage; acetylcholine in the reticular activating system stimulates activity in the awake state, but is also highly active during REM (in NREM it stimulates connectivity of the hippocampus and cortex); dopamine has an involvement sometimes in the sleep state and sometimes in the awake state; and orexin which is only produced in the hypothalamus triggers wakefulness, but at night low levels of it might drive sleep and this is linked to the action of GABA in the hypothalamus. The action of the neurotransmitters directly affects neuron firing and stimulation and therefore, different levels of neurotransmitter and the mechanisms associated with those neurotransmitters in brain regions even at the microscale (eg. the rafts described above by Ball) could influence how an anaesthetic agent can work. This is because, although neurotransmitters bind to specific receptors on the neuronal cell membrane surface, anaesthetic agents are believed to have other effects.

Ball described in his article some of the biochemical affects that anaesthetic agents are supposed to elicit in the neuronal cell. It has been reported that anaesthetic agents in general affect only certain neurotransmitter receptor types, eg. GABA A type receptors as target for the agents propofol and etomidate and NMDA R receptors in the cortex, thalamus and brainstem regions. Ball suggested one effect of the binding of the anaesthetic agent was its effect on the ´critical temperature` of the cell membrane. This is an important feature in action potentials and cell firing where localized changes in membrane fluidity can affect the physiological structures of membrane components, movement of components within the membrane and vital firing mechanisms such as clustering of receptors, ion channel opening and exocytosis of neurotransmitters through vesicle binding to the cell membrane. ´Critical temperature` is defined as the optimal membrane temperature (or critical membrane energy state) which would allow the vital functions to take place and although tempting to think this might be over the whole cell membrane it is more likely that it occurs locally in small nano-domains at certain times during the cell firing process and cell recovery.

The ´critical temperature` (or ´critical membrane energy state`) is likely to occur through the activity of molecules within the membrane and by being active then a higher temperature or state is achieved. With reference to the action of the anaesthetic agents, activity is likely to be via the lipid polarity of the membrane molecules that make up its physiological structure, eg. phospholipids and their electron status achieved through the biochemical groups eg hemes, Fe-S bridges of which they are composed. Just like Turin suggested, the anaesthetic agents would provide electrons through their binding. Binding of groups, molecules, hydrogen ions and electrons cause configuration changes in the proteins and other molecules of the membrane with each tertiary and quartenary conformational change giving different activity to its normal state. This supports the modern lipid hypothesis that lateral pressure distribution can be changed by anaesthetic binding, ie. conformational changes are elicited that affect the activity of the molecule or area in question. Interaction could increase the ´temperature` or energy status of the molecules so that the cell firing is either depressed or activated. It is likely that critical changes occur in small micro-areas and this supports the proposed nano-domains or lipid rafts associated with neuronal firing. It also supports the idea of the action potential stage of neuronal firing only being achieved when a threshold of firing has been reached. It is likely that lots and lots of small nano-domains fire which reach a group effect. Naturally, this is difficult to measure although patch clamping of single channels can demonstrate a limited area of the cell membrane due to the size of the pipette, but it is not possible to say how many nano-domains are present.

Although the idea of membrane lipid binding and changes in the critical temperature of nano-domains appears to be a suitable solution to the action of anaesthetic agents, studies have shown that low temperature changes in membranes are not sufficient to cause a change in consciousness. Therefore, the idea of a membrane fluidity effect by anaesthetic agents is more likely and this is supported by the observation that as chain length of the anaesthetic agent grows, there is an increased effect, but only to a maximum length of 6-10 units after which there are no effects. This can be explained by the anaesthetic agent binding at specific points of the membrane, but once reaching a particular size, this binding cannot occur.

Aside from the anaesthetic agent effect on membrane fluidity (or ´critical temperature`), Ball also pinpointed their action on particular ion channels. Ion channels and the flow of ions are important in the action potential mechanism and cell firing and anaesthetic agents have been shown to have a preference for a particular one, the sodium ion channel. Work by Strichartz and colleagues have demonstrated that anaesthetic agents bind to sodium channels preventing an increase in membrane permeability. They report no change in the calcium ion channels, another important channel in depolarization and recovery mechanisms. Experiments have shown that anaesthetic agents bind to the inner side of the channel after normal depolarization preventing sodium ion influx. Therefore, the block is increased with the frequency of nerve impulses and leads to larger refractory periods. Proteins have been shown to have what is described as ´buried cavities`, since binding of isoflurane to sodium channels occurs even if the channel is closed. Since many neurotransmitters have receptors that are linked to sodium ion channels, eg NMDA receptors, acetylcholine receptors this hypothesis supports the loss of firing and ultimately, loss of consciousness observed. However, anaesthetic agents have also been observed to bind to GABA receptors especially the A type and also have been shown to affect G-protein coupled receptors. In the case of GABA A receptor binding as observed with both propofol (Yip) and etomidate (Li), the anaesthetic agent has been shown to bind within the beta subunit at the interface between the transmembrane domain. It may act by promoting the binding of the receptors agonist, GABA which mediates most synaptic inhibition in the brain or indirectly by positively affecting the associated chloride channel resulting in higher influx of chloride ions and hyperpolarization of the cell. In the case of the G-protein receptors, the anaesthetic agents may not bind to or affect directly the membrane bound receptor or G protein complex, but may produce its affect by affecting the activity of an enzyme further down in the cascade mechanism, that of protein kinase C. A reduction in effective protein kinase C would prevent activation of the post-synaptic mechanisms that control calcium ion release and hence, correct functioning of the neuron would not occur. Anaesthetic agents have also been shown to affect other non-membrane bound molecules eg luciferase, cytochrome p450 and even the microtubular proteins, beta actin and beta tuberlin and therefore, although it is tempting to think that their effect is purely membrane related, their actual function may not be so clear-cut, eg. Turin suggested  their action lies at the mitochondrial membrane.

What has been discussed so far are ´pure` biochemical mechanisms for the action of anaesthetic agents, but work by Turin gave alternative suggestions that were described as ´zombie theories`, but should not be lightly dismissed. The idea of electron transfer within molecules causing changes in conformation and with ion channels involved in the transport of positive or negative ions has already been explained and these are ´accepted` biochemical mechanisms, eg. electron transfer in photosynthesis or ATPsynthase function. Modulation of such factors can lead to firing inhibition or stimulation and hence, anaesthetic agents can produce their effects by altering the electron status of molecules or the firing environment. The breakdown of the mechanism to electrons and electron flow is further supported by the observation that the application of a DC electric current can cause a loss of consciousness from lower to higher (back to front) brain areas and can be observed by monitoring brain wave changes and connectivity of areas as the electric current is applied. A return to consciousness may involve an increase of electron transfer above the baseline of normal wakefulness. This electric current observation has been utilized in the use of electric anaesthesia as early as 1961 (Chappel) by vets and more lately, the application of electric current as a local anaesthetic in dentistry (January 2016).

Therefore, we conclude this examination of how anaesthetic agents work by voicing two thoughts: the first, that anaesthetic agents may provide a means of examining cellular mechanisms in more detail especially if fluorescently labelled molecules (or optogenetics, radioactive tags) can be used; and secondly, does the use of anaesthetic agents in experiments actually affect the results being observed and therefore, can in some cases, definitive explanations to neuronal mechanisms be made if they are present?

Since we`re talking about the topic ……………………..

…………….if ECT is carried out under general anaesthesia, is there a ´double electron whammy`  effect on neuronal cell functioning?

…………….as the patient emerges from general anesthesia, the EEG patterns proceed in approximately reverse order from Phases 2 or 3 of the maintenance period to an active EEG that is consistent with a fully awake state. Therefore, would the administration of certain neurotransmitter blockers at these stages indicate how the neuronal firing mechanism re-boots itself?

…………… by Varin and colleagues showed that the administration of glucose induced SWS by activating the VLPN neurons and leading to the closure of ATP sensitive potassium channels. Are the same channels affected with the administration of anaesthetic agents?

……………… by Pigeat and colleagues showed that LTD of intrathalamic GABA A synapses during SWS involved the T-type calcium channel and metabotropic glutamate receptors. Is the same mechanism employed by anaesthetic agents?

………………..application of a GABA agonist (Mesbata) leads to thalamus receptor binding and an increase in theta brain oscillations (1-4HZ) in wakefulness, plus increased REM, decreased sleep spindles and increased the speed of transitions in the NREM stages. It is known that anaesthetic agents affect the GABA A receptor and therefore, would the same observations be seen with them as for the GABA agonist?

Posted in anaesthetic agents, cell membranes, ion channels, Uncategorized | Tagged , ,

blocking hippocampal AMPAR removal prevents forgetting

Posted comment on ´Blocking Synaptic Removal of GluA2-Containing AMPA Receptors Prevents the Natural Forgetting of Long-Term Memories` by P.V. Migues, L. Liu, G.E.B. Archbold, E.O. Einarsson, J. Wong, K. Bonasia, S.H. Ko, Y.T. Wang and O. Hardt and published in Journal of Neuroscience 36(12) p. 3481


Migues and colleagues investigated whether blocking the removal of GluA2-containing AMPA receptors (GluA2/AMPAR) of neuronal cell membranes prevented the loss of long term memories. They looked at two areas in particular: the rat dorsal hippocampus and infralimbic cortex and used two peptides, GluA23Y (control GluA23A ) and G2CT fused to specific peptide sequences for cell permeability, to introduce interference with the receptor endocytotic process.

Long Evans rats were used in most of Migues and colleagues memory experiments and these underwent bilateral cannula placement using the standard reference atlas. The rats were infused with either control substance or the interference peptides, GluA23Y or G2CT. Habituation to the experimental conditions was followed by specific sets of experiments. For the object location recognition memory test, the rats were subjected to 7 times 2 copies of the same object. After a probe with 1 object removed, Migues and colleagues carried out their experiments. The rats were tested for object location memory and were scored according to the time they spent carrying out exploratory behaviour of the novel and familiar objects. An interval of 14 days was introduced between the learning phase and the recall/testing phase. In the case of the appetitive conditioned place preference (CPP) test, 2 compartments were used with Fruit Loops as reward. Performance was assessed by measuring the time the rats spent in each compartment, hence preference for one or the other was recorded. Contextual fear conditioning was carried out using vanilla and peppermint oils and electric foot shocks and performance was measured according to the amount of freezing (blocks of 30 seconds) exhibited.  The auditory fear conditioning experiments were carried out in a similar manner to the contextual fear experiments except tones (3 tones at 5HZ) were used. For the electrophysiological studies, Sprague Dowley rats were used instead of the Long Evans rats. Long term potentiation (LTP) was induced in whole hippocampal CA1 cells by applying 200 pulses of repetition stimulus and voltage clamping cells at -5mV whereas cells were exposed to 300 pulses at – 45mV for depotentiation. Results were assessed using one way ANOVA for EPSC amplitude at 5 mins for basal transmission, 20 mins for LTP and 60 mins for depotentiation.

Migues and colleagues found in their object location recognition memory tests that control rats were more likely to explore the novel location if they still had the memory for the old object. They found that this preference for the old object decreased with time until 7 days after learning and concluded that no long term memories existed after 8 days. Therefore, control rats and rats that had received the infusion of the interference peptide, GluA23Y , were tested 14 days after training. Migues and colleagues found that rats where the dorsal hippocampal GluA2/AMPAR removal had been blocked with the interference peptide GluA23Y  preferentially explored old objects at new locations. Hence, the normal time-associated forgetting of established, long-term spatial memories relating to object location had been prevented by the administration of the AMPAR removal blocker. Repetition of the experiments at 7 days after learning produced the same results. When the peptide G2CT which binds to a different site in the removal process was used instead, the authors again detected a significant preference for exploration of the old object moved to the new location. Therefore, blocking the removal of dorsal hippocampal GluA2/AMPARs with interference peptides GluA23Y or G2CT during an interval after learning did not affect acquisition of new object locations in a spatial memory test, but did prevent loss of established memories.

Blocking the removal of dorsal hippocampal GluA2/AMPARs with interference peptides GluA23Y or G2CT also prevented loss of associative memories of food-reward in the authors` conditioned place preference (CPP) test. The use of the AMPAR antagonist, CNQX, showed that AMPAR activation was linked to conditioning in the hippocampus. After 10 days no conditioned response was visible under normal conditions, but with infused GluA23Y the authors found that the CPP forgetting had been blocked. This was demonstrated by the rats showing preference for the side that had previously been supplied with food.

The authors also investigated if dorsal hippocampal GluA2/AMPAR removal was involved in behavioural changes observed with time. In auditory fear conditioning experiments, Migues and colleagues found after 2 weeks fear expression was the same. When they infused GluA23Y into the dorsal hippocampus during the 2 week retention interval, they found the rats exhibited generalization of contextual fear expression ie.  the loss of context discrimination had been blocked. Rats infused with the control substance showed the same contextual fear levels. Infusion of GluA23Y into the infralimbic cortex area instead of the hippocampus after extinction of auditory fear prevented the spontaneous recovery of the fear memory conditioned response after the extinction period.

Migues and colleagues also investigated possible physiological mechanisms of memory decay using hippocampal slices and the electrophysiological patch clamp technique. EPSCs were evoked in CA1 cells and LTP and then depotentiation were induced. The authors found that bath application of GluA23Y prevented depotentiation, but not the induction of LTP or basal transmission. Application of the control substance, GluA23A, did not prevent depotentiation.

The authors concluded following their experiments that removal of GluA2/AMPARs erased consolidated long-term memories in the hippocampus and other brain structures over time and this contributes to the displayed behaviour. Blocking the activity dependent removal process using infusion of the peptides GluA23Y and G2CT that interfere with the internalization process of AMPARs prevented the time-associated loss of memory. This suggested to them that dysregulation of the AMPAR removal process could hinder the time-associated decline of spatial memory and improve cognition in this area.


This article is interesting because it links memory retrieval with hippocampal AMPAR population. It is known that hippocampal AMPARs are required for memory formation of spatial memories and conditioning memories and according to Migues and colleagues, forgetting of this type of information is linked to the removal of these AMPARs in a time-dependent manner. By maintaining AMPAR number in this region then loss of memories of these memory types is prevented, ie. information learnt at a previous time can still be recalled. Information learnt in these cases relates to the memories of object and place for spatial memory and for conditioning an extension of this by adding a temporal element followed by additional information relating to the reward (or fear). Forgetting spatial memory and the extinction of conditioning occurred naturally in Migues and colleagues experiments with no information retained 14 days after learning had stopped. This was demonstrated by the exploration of the old object in the new location for the spatial memory task being greater than the old object in its old location. Administration of an AMPAR removal blocker led to equal exploration of the old object in both new and old locations. Therefore, it was concluded that blocking the removal of the hippocampal receptors meant that long-term spatial and conditioning memories had been maintained.

The question is how does this occur. We know that AMPARs are linked to spatial or sequence memory (object and ´reward` as in conditioning) and are required for information input, processing and memory formation. Their capability is linked to the physiology of the hippocampal region itself. In the case of spatial memory two pieces of information, ie. place and object stimulate activity in specialized hippocampal neurons called place cells (work by McNaughton, O´Keefe), and these cells exist alongside neurons representing the object information, location and timing. Firing of the neurons leads to AMPAR activity as expected in normal neuronal firing mechanisms. Information coming in fires neurons in the entorhinal cortex area (EC2) leading to firing of the dentate gyrus (DG) followed by the CA3 region, or straight from the EC2 to the CA3 region. DG to CA3 firing relies on the activation of large mossy fibres and glutamate release, but more important in terms of AMPAR is the firing of the small terminals and filopodial cells. This firing results in activation of GABA interneurons with GABA neurons providing the most information in this region. These neurons produce short term responses to repetitive stimulation leading to glutamate release which causes the post-synaptic AMPAR activation reported in the literature. Ca2+ permeable or impermeable AMPAR leads to NMDAR independent or dependent LTP and AMPAR downregulation (work by Nicoll and colleagues). This will have an influence on information input and processing of future events. My own view is that stimulation of the CA3 leads to neurogenesis and glutamate receptors formation leading to LTP in newly formed cells which then switch to LTD (or the area switches to cells favouring LTD) and finally endocytosis of the receptors and cell apoptosis. This is supported by reports that as far as the CA3 to CA1 network is concerned there are multiple synapses;  in the CA1 both LTP and LTD are demonstrated; and the areas show differences in processing new and old information. New information is given priority and causes the effect with repetition leading to memories formed higher up the brain area hierarchy based on the priority given at this stage. The area and neuronal cells then switch into LTD mode once these long-term memories are established higher up in the appropriate cortical areas.

The LTP observed in the CA1 region does not reflect memory storage in the region itself as some proponents think, but instead is likely to act more like a signal and the area then as a relay station. A glutamate receptor link to memory function is well known here demonstrated by gene targeting mice leading to defective glutamate receptor formation demonstrating impaired maze learning. More specifically, the associated LTP is linked to the insertion of AMPAR (work by Derkath, Plant, Ehninger, Rauer for example) resulting in increased synaptic strength and increased susceptibility of cells to depolarization. Plant reported that calcium permeable AMPAR inserted first are without the Glu2R subunit which appears later which could explain the time difference using the Glu2R subunit specific peptide. Increased synthesis of AMPAR occurs via increased synthesis of both GluA1 and GluA2 subunits by 4E-PB2 deletion and AMPAR trafficking to the cell membrane requires, for example, the kinesin-kinesin superfamily 16B (ILIF 16R) for the microtubules; palmitoylation of AMPAR linked scaffold protein kinase A anchoring protein (AKAP) 79/150, RAb 11 and other effectors as well as  A2R, PKA and actin polymerization (work by Zhu – stimulation and trafficking). PIP3 turnover is required at the synapse to maintain the clustering of AMPAR  into what is thought to be nano-domains and this is assisted by MAGUK proteins. Calcium ion entry is linked to the membrane situated AMPAR and the calcium ion channel is reported as a L-type channel which leads to slow after hyperpolarization (Kim). These

calcium-permeable AMPA receptors (CP-AMPARs) contribute to the synaptic plasticity of the area (McGee). The capability of the CP-AMPAR lies in its structure with the prototypical transmembrane AMPAR regulatory protein stargazin, which acts as an auxiliary subunit, enhancing the receptor function by increasing single-channel conductance, slowing channel gating, increasing calcium permeability, and relieving the voltage-dependent block by endogenous intracellular polyamines. The overall effect of the glutamate release on cell activation is that the AMPAR trafficking and membrane insertion results in the cell being more sensitive to the firing stimulus (LTP).

However, the initial LTP observed in the CA1 and CA3 is replaced by LTD which is where the active synapses decrease in firing effectiveness. LTD is normally linked to sequence memory in cerebellum, but it is also linked to changes in memory and learning associated with hippocampus functioning in a mechanism similar to that followed in the cerebellum system. In the cerebellum, LTD arises when 3 intracellular signals occur at same time: a rise in internal calcium ion concentration due to climbing fibre activation, a rise in internal sodium ion concentration due to AMPAR activation and activation of protein kinase c due to metabotropic glutamate R activation. These signals result in a decrease in AMPAR, and hence a decrease in the opening of postsynaptic AMPAR channels. Similar results are obtained with the hippocampus. Riazo and colleagues showed that inflammation in other parts of the body leads to cognitive impairment and this was attributed to induced changes in the hippocampus synaptic transmission. Altered postsynaptic effects were observed. Increased AMPAR and NMDAR currents were demonstrated with increased mGluR2 receptors and decreased LTD and LTP. In other experiments LTP/LTD changes were observed in AD mouse models which may reflect defects in the neuronal plasticity processes. Experimenters found that in young 1 month old Tg mice, LTP is enhanced at the expense of LTD, but in 6 month old adults the phenotype is reversed to promote LTD and reduce LTP. These observations were attributed to altered AMPAR phosphorylation and the appearance of calcium ion permeable AMPAR.

In the CA1, LTD and decreased cellular PSD95 levels are observed and in my view is part of the mechanism to ´switch off` the CA1 neurons after stimulus firing and memory formation. Such cells are then culled (or more susceptible to culling) by the microglia in order to maintain the excitability of the area. The process is linked to the level of internal calcium. Increases are due to modest NMDAR activation and prolonged increases in calcium ion concentration ie. due to prolonged stimulation lead to activation of protein phosphatases resulting in AMPAR dephosphorylation and the induction of LTD. The post synaptic AMPARs are internalized at the synapse leading to decreased PSD95. This view is not totally supported with Babiec suggesting that LTD induction is instead attributed to an ion channel-independent, metabotropic form of NMDAR signaling. It was found that the induction of LTD in the adult hippocampus is highly sensitive to extracellular calcium ion levels and that MK-801 blocks NMDAR-dependent LTD in the hippocampus of both adult and immature mice. In addition,  MK-801 inhibits NMDAR-mediated activation of p38-MAPK and dephosphorylation of AMPAR GluA1 subunits at sites implicated in LTD. Therefore, these results indicate that the induction of LTD in the hippocampal CA1 region is instead dependent on ionotropic, rather than metabotropic, NMDAR signalling. These differences may be attributed to the amount of stimulation occurring at the time since differences in receptor internalisation appear to exist. Glebov showed that rapid forms of internalisation of AMPAR  during LTD require clathrin and dynamin, whereas they were not required in slow homeostatic forms of synaptic plasticity. In this case AMPAR trafficking is blocked by a Rac1 inhibitor and is regulated by a dynamic nonstructural pool of F-actin. In the experiments conducted by Migues and colleagues, receptor removal is prevented by the administration of either GluA23Y or G2CT interference peptides, hence the cell remains extra sensitive to the stimulus. When forgetting occurs, memories are still ´there`, but cannot be accessed and this has been proven with experiments carried out with genetically modified genes responsive to blue light. The memory is forgotten, but as soon as the cell is shone with blue light then the conditioned fear returns.

Therefore, the hippocampus AMPARs are responsible for information input and processing in initial memory formation and this is attributed to the areas unique structure, functioning and connectivity.  Information is ferried from the EC to the hippocampus to the fornix to the mammillary bodies to the anterior thalamus. Beta brain wave synchronisation of the lateral EC and hippocampus is required for initial information encoding and beta wave synchronization of the LEC and mPFC areas for remote memory recall (work by Vetere). It was found that dendritic plateau potentials were produced by an interaction between properly timed input from the EC and hippocampal CA3 region. These conjunctive signals positively modulate the firing of previously established place fields and rapidly induce new place field formation to produce feature selectivity in the CA1 which is a function of both entorhinal cortex and CA3 input. Bittner says that such selectivity could allow mixed network level representations that support context-dependent spatial maps. This functioning is susceptible to age differences. The appearance of different field maps in the CA1 of hippocampus means that older rats ´remap` their environment each time they are exposed to it and there is a reliance on self-motion whereas younger rats use previously formed models and rely on visual stimuli.

This experiment gives an indication of how AMPARs influence memory recall. Memory recall is linked to hippocampal functioning, but not with memory storage since hippocampal lesions leads to the loss of new information, but retention of familiar environments (Winucor). This supports the views of Pinel who stated that the hippocampus is involved in the consolidation of long-term memory for spatial location, but not in its storage. Although storage of the memories occurs at the higher cortical areas, the hippocampus is still required in memory retrieval. It has been found that the both the medial prefrontal cortex (mPFC) and the hippocampus are required for the retrieval of spatial memory since, for example the blockade of 5HT2Rs in the mPFC affects the retrieval of the object in context memory, but not in a single object recognition task. Connectivity between the mPFC and the hippocampus is required during the retrieval task (Beckenstein). In Migues and colleagues experiments object recognition is part of the spatial memory and conditioning tasks and this requires appropriate functioning of the hippocampus and the involvement of AMPARs. This was proven by the observation that inactivation of the dorsal hippocampus after training impairs object-place recognition and memory, but enhances novel object recognition. Repeated exposure is not affected by the inactivation of the dorsal hippocampus (work by Olioveirol and colleagues) because long-term memories have been formed elsewhere and retrieval requires activation of these areas. Haetting refined the observation by showing that inactivation of the dorsal hippocampus with muscimol prior to retrieval had no effect on long-term memory in object recognition experiments, but completely blocked the long-term memory for object location demonstrating that place cells and cells stimulated by object features were not involved, but the cells involved in location were. Memory consolidation and reconsolidation is a constantly evolving process with remapping occurring on each reencounter as seen in Migues and colleagues experiments where the same object is constantly being moved to new locations. For such consolidation and reconsolidation processes protein synthesis is required and NMDAR activity mediates trafficking of AMPARs taking place during the recall process (Lopez).

The continuing matching of new information to reactivated old information requires working memory which requires hippocampal activity. Maintenance of multiple items are associated with hippocampus activation (hippocampus-dependent working memory) while maintenance of individual items induces deactivation. Processing leads to long term memory if the hippocampal activity patterns match those previously observed (Fixmacher) and the link to AMPARs comes through the observation that NMDAR antagonists decrease working memory (Takadi) and the level of task irrelevant information is affected by NMDA antagonists (Gage). In the experiments carried out by Migues and colleagues there is a certain level of informational overlap in the form of new location/old location, but the object information is the same. It is believed that the DG plays a role here with pattern separation linked to neurogenesis (Deng). The DG role is at a maximum when there is maximum similarity between object and place pairs and minimal when there is little overlap (Lee). This supports earlier experiments where the hippocampus is known to play a role in familiarity experiments as well as recollection (remember/know) (work by Song, Jeneson, and Smith for example). Here the hippocampus plays a comparator role capable of individualizing representations of overlapping inputs (Zeamer).

So far we have discussed only the hippocampal AMPAR role in spatial memory tasks, but it is clear from Migues and colleagues experiments that they are also involved in fear conditioning. Fear memories are normally linked to amygdala functioning, but the hippocampus also plays a role since there is for each conditioning event information input and processing. There is continual analysis of sensory input to learnt material and in the case of reward, if none is presented there is no reinforcement of the memory and extinction occurs. Migues and colleagues experiments show that extinction of the conditioned response involves AMPAR removal since once it was blocked with the AMPAR removal interference peptide then the fear memory was retained. This means that post-synaptic AMPAR population was maintained and played a role in retrieval of the fear memory.This is against the established view since normally fear conditioning (consolidation, but not acquisition – Liu) is linked to CA1 activity and regulation of NMDAR  receptor number. It is reported that in this case, NMDARs are induced and LTD requires activation of caspase 3 by cytochrome c released from mitochondria in a process promoted by BAX. (BAX induces cell death by apoptosis normally, but does not play this role in LTD.)  Liu showed that in fear extinction a new memory is actually formed rather than erasing the original fear memory. Exposure to novelty (environment) facilitates the transfer of short-term extinction memory to long-lasting memory, but the mechanisms are to date not understood. Therefore, the established view that extinction of fear memory occurs through NMDAR down regulation is extended by Migues and colleagues experiments which show that down regulation of AMPARs also plays a significant role and if the endocytosis of the AMPAR is prevented then extinction cannot occur.

Therefore, Migues and colleagues experiments confirm what we already know about role of glutamate receptors in synaptic plasticity and their role in spatial memory and conditioning. However, it also demonstrates the critical role of the AMPAR aside from its function in LTP and memory formation. Migues and colleagues experiments show that time-related forgetting associated with down-regulation of this type of receptor can be prevented by blocking their removal from the post-synaptic membrane. However, it is unlikely that control of memory loss is focused completely on the removal of this one type of receptor. It is possible that they play a role in the initial stages and this may be important if we consider the case of Alzheimer illness. In Alzheimer illness there is reported hyperexcitability of the EC and hippocampal areas which should theoretically be linked to AMPAR up-regulation and PSD95 increase representing increased synaptic sensitivity. However, Alzheimer illness is linked at later stages with loss of memory which suggests a lower level of AMPARs supporting Migues experiments and therefore, the situation may not be clear cut. AMPAR down regulation in the early stages of the disease (ie. before the symptoms show) may provide an answer. Administration of AMPAR endocytosis blocker may if practically possible provide a beneficial short term effect only. The other conclusion from Migues and colleagues experiments is that the mechanisms for object vs spatial memory are probably slightly different. Old object/ old location vs old object/new location place cells in rat may have different control mechanisms to the human where visual information, key and location are not olfactory linked. Promotion of one type may aid the memory of the other. This may provide another mechanism by which forgetting may be prevented.

Since we`re talking about the topic…..

…..brain waves such as delta, gamma and beta are reported as showing temporal coordination, therefore can we assume that the use of the infused interference peptides would produce a measurable change in brain wave firing if observed during Migues and colleagues experiments?


…. LTD is found to be sensitive to calcium extracellular MK801 which blocks NMDAR  mediated p38 MAPK and dephosphorylation of the AMPAR subunit Glu A1. Therefore, if MK801 is administered would the same results be seen as with the interference peptides?

……can we assume that the action of tetrahydrocannabinoids which increase spatial memory by altering dopaminergic pathway activity in the PFC (Makele) would increase spatial learning, but have no effect on spatial memory forgetting?

…..since the stimulation of the perirhinal cortex at 10-15Hz causes animals to treat a novel image as familiar (observed by decreasing the time spent looking at an image – Ho), would using this type of stimulation have any effect on the experiments carried out here?


Posted in conditioning, glutamate receptors, hippocampus, neuronal firing, recall, spatial memory, Uncategorized | Tagged , , , , ,

consciousness and attention are not fully dissociable in all circumstances

Posted comment on ´Against the view that consciousness and attention are fully dissociable` by G. Marchetti and published in Front. Psychol. 15th February 2012, doi / 10.3389/fpsyg.2012.00036


Marchetti in his review article provides argument against the view that consciousness and attention are fully dissociable. He states that there are various forms of consciousness and attention and not all the forms of attention produce the same kind of consciousness. In the case of low level attention, this form can exist with or without consciousness, but in the case of top-down attention this form can only exist with. This view goes against the opinion of Koch and Tsuchiya (2006) for example who describe four possibilities of top-down attention and consciousness including top-down attention with and without consciousness. According to Marchetti, the high-level top-down attention without consciousness form of Koch and Tsuchiya occurs because of a failure to recognize the different types of top-down attention and consciousness that exist. Therefore, attention cannot be considered the same as consciousness, and attention in some form is always required, hence consciousness and attention is not in all situations fully dissociable.

Marchetti begins his article by reviewing evidence of the close correlation between attention and consciousness. Selection means that something can be attended to and can be isolated from the other features of the event with conscious awareness completely on the attended feature whilst the other features are ignored. Also, both visual and temporal perception can be modulated by attention, eg. attention alters phenomenal appearance by boosting the stimulus contrast (Liu, 2009). Marchetti goes on to describe inattentional blindness which was originally explained by the presence of unconscious processing, but with no conscious perception and no attentional processing. However, alternative explanations have been put forward with memory lapse being one of them. In this case, the individual is assumed to forget about the distracting stimulus due to the delay between its presentation and the individual being questioned. Another alternative explanation is perceptual load. In their experiment with the development of a change detection flicker task, Rensink et al. (1997) found  that the identification of changes was extremely difficult not due to the disruption of perceived information or stored information, but due to the level of attention applied.  Change detection appears to be dependent on the level of perceptual load, eg. low load is linked to awareness of irrelevant stimuli, but with high load there is no such awareness. This confirms the view that attention is needed for the detection of change.

Having shown that inattentional blindness is linked to the lack of conscious awareness of stimuli that may or may not be attended to, Marchetti goes on in his review article to investigate more thoroughly the view of Koch and Tsuchiya that consciousness can be dissociated from top-down attention. Marchetti believes that their view is only partly true since there are cases of consciousness in the absence of a certain form of top-down attention, but in the presence of another form of attention such as bottom-up and there are no cases of conscious awareness in the complete absence of some form of attention. Marchetti explains this by saying that Koch and Tsuchiya failed to take into account the different forms of attention and consciousness that exist. For example, there are least 2 different forms of top-down attention, eg. focused and diffuse (or distributed attention), each with different characteristics. There are also different forms of consciousness such as ambient awareness where there is a general awareness of the environment and focal awareness where there is a detailed awareness of a scene, but not necessarily with access to the Self. Just like with attention, characteristics of the consciousness forms differ.

Koch and Tsuchiya (2006) gave several examples as evidence for their view of dissociable attention and consciousness, eg. attentional blink and gist and these examples were given alternative explanations by Marchetti in his review article. In the case of attentional blink, performance at detecting the second stimulus (T2) improves with a longer delay between its presentation and the presentation of the main stimulus (T1). This is thought to be because processing of T1 takes up the limited attentional resources so that either access to these resources is denied for T2, or the representation of T2 is so vulnerable that it easily suffers from the interference of simultaneous distracting features surrounding it. However, less than optimal focusing on T1 actually led to improved T2 detection (Olivers and Nieuwenhuis, 2005) and although Koch and Tsuchiya said this was due to top-down attention and consciousness opposing one another, a more accepted explanation was given by Srinivasan in 2008. Srinivasan described a diffused (distributed) attentional strategy that under certain conditions appears more appropriate than focused attention. One such condition is when subjects know that they need to consider a large number of items in order to report a second target stimulus. As attention widens to incorporate the extra task, it may also widen temporally and hence, includes T2 in the series of stimuli. However, Marchetti explains that this explanation does not take into account the overall improvement in T1 performance so it is probably not just diffused attention, but also a temporary increase of the allocated attentional resources owing to the difficulty of the task. This temporary increase may be related to arousal since it was found that decreased or increased arousal makes the attentional system more susceptible to other input, including T2. Another explanation was given in that the task itself may induce a positive emotional state, which has been shown to improve performance with some cognitive tasks.

These explanations were also given for Koch and Tsuchiya`s other evidence for fully dissociable consciousness and attention that of animal and gender detection in a dual task and gist. In the case of gist, Marchetti describes the use of diffused attention and states that gist is evidence of another form of consciousness (ie. primary consciousness) where there is awareness, but not the language capability to perceive it or describe it. Diffused attention was also given as an explanation for the pop-out and cocktail party effect.  Top-down attention was found to be necessary for the subliminal pop-out effect whereas the cocktail party effect required some form of attention, either top-down or bottom-up for consciousness to occur.  Although the cocktail party effect was interpreted by Umilta (1994) as having attention and consciousness as independent systems with the object being perceived consciously in a direct manner without attention, Mack and Rock (1998) disputed this by showing that increasing the inhibition of attention to an object led to a decrease in probability that the object would be perceived. Hence, Marchetti concluded in his article that some kind of attention is always involved in conscious perception even in situations where high emotional values such as one`s own name are applied. In the absence of attention, there is no conscious awareness.

In the case of iconic memory, Marchetti quotes in his review article the work of Lamme (2003) who proposes that there can be consciousness without attention since the attentive selection process operates at a later stage than consciousness and that attention does not determine whether stimuli reach a conscious state, but determines whether a conscious report about stimuli is possible. Lamme`s model supports Block`s 1996 view of the existence of two distinct kinds of awareness: phenomenal and access awareness and the distinction in sensory memory between iconic memory and working memory. Lamme quotes work on change detection experiments saying that attention is a selection process that determines if the stimulus goes from phenomenal consciousness to access awareness. The model is based on observations that there are different levels of processing that stimuli can reach and that these different levels of processing rely on an early distinction between conscious and unconscious stimuli. According to Marchetti, the Lamme model overlooks the fact that both attention and consciousness can assume a variety of different forms. For example, if Lamme says that non-attentional selection mechanisms lead to unconscious processing of stimuli then preliminary attention means that information might be processed even if not consciously experienced. Marchetti`s explanation is based on attention being necessary for consciousness, but various levels and types of attention are possible. In the case of change detection, Marchetti explains the finding that a change in location cued during the blank ISI leads to improved performance is not proof of consciousness without attention, but instead confirms that there is an early component of attention (an exogenous one) that can capture a specific item in the iconic memory. Lamme also stated in 2003 that a view of a visual scene is experienced with a ´richness of content` that goes beyond what can be reported when questioned. Marchetti explains this ´richness of content` as occurring when the participant`s initial application of attention to a presented array of items triggers a ´primary` (non-verbalized), rich form of consciousness of the visual scene. Subsequently, the content of the primary consciousness can be verbalized because of the use of an additional amount of attention due to the cue.

Having investigated the view that there can be no consciousness without some form of attention, Marchetti goes on in his review article to look at whether there can be top-down attention without consciousness. Some researchers affirm the view because attention can generate or modulate unconscious phenomena. Naccache et al. (2002) state that it is possible to elicit unconscious priming in a number-comparison task, but only if the subject’s temporal attention is allocated to the time window in which the prime target pair is presented. Unconscious priming vanishes when temporal attention is focused away from this time window. Sumner et al. (2006) state that attention modulates neural sensorimotor processes that are entirely separate from those supporting conscious perception and Bahrami et al. (2008) affirm that in tasks of low perceptual load any spare capacity from the processing of the relevant stimulus spills over to the processing of irrelevant stimuli regardless of whether or not subjects are conscious of the representations. However, Marchetti`s view is that attention can also generate unconscious phenomena, but is not per se evidence that there can be top-down attention without consciousness. Consciousness only occurs when top-down attention is at a lower level that it has not reached threshold consciousness. Observations of top-down attention without consciousness comes from, according to Marchetti, the confusion of the perception of consciousness absence with the absence of perception, or by overlooking the existence of the many forms of attention and consciousness.

    In the case of confusing the perception of consciousness absence with the absence of perception, Marchetti explains that a person can be aware of something without being aware of something else, or even that a person can be aware of not being aware of something. Mole (2008) said that cases, in which the subjects are on the lookout for something that does not appear, are not cases of attention without perception. They are rather, cases where the subject perceives that nothing has yet occurred. Overlooking this means that a mistake is made between perception of absence and absence of perception. Therefore, some experiments provide evidence of top-down attention in the absence of conscious awareness of something, but in the presence of conscious awareness of something else. In the case of motion-induced blindness (MIB), paying more attention to the MIB target increases the probability of its disappearance from consciousness, ie. the more the participant looks at something, the more he sees. However, in this case the MIB target is an illusion and hence, this demonstrates that top-down attention with consciousness can occur in the absence of something.

Another explanation for misinterpreting top-down attention without consciousness is that the existence of various forms of attention and consciousness are overlooked. Marchetti uses the example of blindsight and a subject named GY to refute the claim that there is top-down attention in the absence of any form of consciousness. He explains that although GY may have verbally reported no awareness of any cues, it might not have meant that he had no conscious experience of anything. Verbal reporting requires a higher order reflective form of consciousness, but GY could have been experiencing primary consciousness and therefore, there would be endogenous attention without reflective (autonoetic) consciousness, but with direct, primary (anoetic) consciousness.

Marchetti continues his review article with a look at whether any kind of attention can be dissociated from consciousness and concluded that it is possible with low level attention (preliminary attention), but not with high level top-down attention. Marchetti quoted Velman`s 1991 work where the aim was to confute the conventional assumption that preconscious processing is identical to pre-attentive processing and conscious processing is identical to focal-attentive processing. Velman based his view on evidence that preconscious processing is not inflexible, not limited to simple, well-learned stimuli, not non-attended or pre-attentive since preconsciously processed stimuli are subjected to sophisticated and elaborate analysis. In this way, preconscious cues may receive attentional resources even though they may not enter consciousness. Therefore, Marchetti concludes that preliminary attention and consciousness can be dissociated. This is confirmed by other studies, eg. dichotic listening tasks, shadowing tasks and Stroop effect which show that stimuli can be preconsciously processed if given at least a minimal level of attention. Subjects pay a certain although low level of attention to the to-be-ignored stimuli even if instructed not to and this is possible through distributing the focus of attention and allocating a small level of attentional resources to them as described above.  This supports Damasio`s view (1999) and provided him with an acceptable explanation of some diseases such as akinetic mutism, epileptic automatism and advanced stages of Alzheimer’s disease. According to Damasio (1999), there is evidence of dissociation between low-level attention and consciousness because the sufferer exhibits some basic signs of attention (eg. the ability to form sensory images of objects and execute accurate movements relative to those images), but it is not related to the sense of Self, to thoughts relating to wishing, considering or, future time. Therefore, this form of attention is distinguished from high-level attention, which extends in time and whose focus on appropriate objects is indicative of consciousness. Therefore, Marchetti concludes that consciousness can be dissociated only from low-level attention (preliminary attention), whether of an endogenous or exogenous kind, but it cannot from high-level top-down attention.

In summary, Marchetti`s review article provides argument against the view that consciousness and attention are fully dissociable in all situations. By giving alternative explanations to common attention/consciousness experiments he gives evidence that there cannot be consciousness without some form of attention, but there can be different forms of both with different characteristics. The experimental results obtained explore these various forms of both consciousness and attention. From these experiments and conditions he states that there cannot be high-level top-down attention without consciousness and arguments put forward against this view come about by the failure of researchers to take into account the differing forms that top-down attention and consciousness can assume. However, Marchetti also recognizes that there can be low-level attention (preliminary attention) with or without consciousness. Therefore, Koch and Tsuchiya`s view of dissociable consciousness and attention is only correct to a certain point.


Marchetti in his review article dismissed the idea of a fully dissociable consciousness and attention for every situation – a view that we all can understand if we look at our daily lives. We know that in our experienced activities conscious awareness is not always the same as what is being attended to. For example, I know I can talk and drive at the same time and my attention is split between watching the road, automatically changing gear and talking about something. Or, if I think deeply about something and try and solve a problem, I may have full conscious awareness and full concentration on the task, but I still may be jogging or doing the washing up. Or, if I accidentally drop a glass I`m already moving trying to catch it before I consciously realise it`s falling and tell myself I need to move. Therefore, Marchetti`s conclusions were correct about non-dissociating attention and consciousness and also about the different forms of consciousness and awareness that exist. I know that in certain circumstances I need full attention on a task and conscious awareness is at the highest level, eg. playing a piece of music for the first time. In this case, higher order top-down attention then exists with conscious awareness. However, as described in Marchetti`s review, there may be occasions when all is required is low level preliminary attention, but with no awareness. What makes this topic interesting is the balance of conscious, unconscious, and even preconscious events, the shifts of consciousness and the link between conscious awareness and top-down and bottom-up attention. If we understand why something is attended to, or conversely what is ignored and how this is linked to conscious awareness, we can possibly manipulate the situation to our advantage. This type of knowledge is already being applied to daily life. For example, vast amounts of money are spent on things like advertising or educational methods – money spent to increase conscious awareness and attention to increase product buying or learning for example. It can also be important in situations where individuals suffer cognitive deficiencies – if we can improve the quality or quantity of the event attended to then maybe increases in memory or information processing will occur.

Therefore, in order to improve the quality or quantity of attended information, we have to investigate what determines what is attended to and how the relevant neurochemical mechanism works. What we attend to or not attend to depends on the physical functioning of two types of attentional system – the top-down attentional system and the bottom-up. These may have common cell types and common cell neurochemical mechanisms, but they differ in the activity and connectivity of the various brain areas involved. High level attention involves the dorsal brain areas, frontal and parietal areas, and the prefrontal cortex plus known sensory orienting systems such as the frontal eye fields and intraparietal sulcus. In this case, there is top-down voluntary recollection of information and selection and processing of material requires activity in the central executive, working memory and cortical memory areas. In contrast, bottom-up attention requires activity in ventral and parietal areas and is linked to involuntary memory recollection. There is also a difference between attention that is focused and a diffuse (or distributed) type described by Marchetti in his article in that with the former, attention is focused on one event whereas, in the latter,  attention is distributed over the general event, a bit like a ´group` impression. Although not mentioned by Marchetti in his review article, there is a third attentional state which exists when the emotional state of the individual is fear. In this situation, differences in quality and quantity of incoming and processing of information are observed in comparison to the individual`s normal emotional status.

The idea that physiological systems give rise to mental representations is not new. In 1890, James hypothesized that his experience was what he agreed to attend to and in this case, ´experience` means awareness and ´attend` means attention and here, the only form of attention meant is that of top-down. For either high or low level forms of attention, the first 270 milliseconds of a visual event are the same independent of whether the feature is attended or not. Bundesen`s Neural Theory of Visual Attention of 1990 described two waves of processing of this incoming information. The first wave involves attention distributed non-selectively over the visual field leading to a saliency map since perception, memories and values are applied to the objects. This leads on to the second wave where there is selective competition to populate the short-term memory store by allocating attentional resources according to the ´weight` of the stimulus taken from the saliency map. Therefore, in every sensory event, some features are attended to whereas others are not and preconscious events may slip to conscious events or may die out. This is equivalent to the fading out of one of the ´multiple drafts` of conscious experienced events.

Attended features are assumed to be fully processed and how much attentional resources are allocated is dependent on difficulty, novelty etc. Features preferred for attendance can depend on the stimuli`s colours, intensities, sizes, the memories and/or the values they evoke and even non-visual factors such as task difficulty and timing. In Marchetti`s review, he states that attention can change the perception of the stimulus. Event characteristics such as greater contrast lead to focus on the attended and longer and earlier timing of focus. This applies to both top-down and bottom-up attention. Features coming from bottom-up attention are accepted as sensory, but still critique, memories, emotions and values and recency and adaptation rules are applied. With conscious recollection in the absence of relevant sensory stimulus, the attention is internal and reflects individual choices, can be relatively automatic (sometimes need to focus on material to retrieve it), and does not require the allocation of sensory bottom-up attentional resources since there is an automatic memory recall process. In this case, the areas required for conscious awareness of successfully retrieved memories are the prefrontal cortex (initiation vs monitoring and maintenance; the ventrolateral regions – items and maintenance), dorsolateral areas (updating and manipulation), medial temporal areas (binding), and the parietal cortex (filtering and selection of material). The interconnectivity of the areas is demonstrated by the shared gamma 40HZ brain waves observed which are initiated by the prefrontal cortex and hippocampal areas and spread out across the relevant areas.

The attentional mechanism for attended and unattended features is said to show three properties at a computational level: a filtering process, which has limited capacity; selectivity, with some features attended and others not depending on stimulus characteristics; and a modulated ease of processing of the selected events. This view was extended by Knudson in 2007 who added the involvement of working memory to Bundesen`s  Neural Theory of Visual Attention. Therefore, the attentional mechanism was said to consist of four components: working memory, competitive selection based on biased competition (eg. stimulus, colour, size), top-down sensitive control (based on memory and value recall and association) and automatic saliency bottom-up filtering (based on stimulus features and automatic recall of associated memories and values). That attention acts as a selecting mechanism for conscious contents and working memory as the specific store supports Dehaene and Changeux`s 2011 Neuronal Global Workspace Theory for consciousness. The involvement of working memory in attention is also supported by the observations that working memory tasks are disrupted by shifts in attention. The working memory buffer in the parietal area is for gating stored information with cortical binding of relational activity. This is likely since working memory is responsible for the manipulation of information, fitting it to recalled memory, perception etc.  and the more informational processing that is carried out the better the memories formed. However, my own view is that working memory is a state where information is malleable and not a process. It provides the condition in which processing can occur, and where processing may mean just the selection of information based on strength of firing and binding.

However, not everything of an event is attended to. Non-attended features means the individual is not aware of them and cannot report them. However, this does not mean that the features are not processed and the level of processing, as Marchetti described in his article in relation to inattentional blindness, is dependent on perceptual load (feature characteristics and value/desire dependent) and the level of resources allocated which is monitored by the prefrontal cortex and cingulate cortex areas. Non-attended features can also be due to diffuse attention where there is no focus,  instead where it is distributed like looking at a big scene, as in the case of gist. Again, processing is possible of the scene and depends on perceptual load and allocation of resources. In the case of non-attended features load may be higher for the focused features or may be non-changing or long-lasting so that resources are allocated to more immediate demands.

Therefore, there may be several scenarios possible regarding attentional system source, level and conscious awareness and functional experimentation and neuroimaging can determine the characteristics of each. The highest attentional level is top-down attention focused on a specific event or activity. In this case, since focus is elsewhere in the simultaneous event, unconscious features are likely to be either from the stimulus (bottom-up based, when the speed of event presentation is quicker than the eye, or when the feature is not as sensory stimulating as the main feature on which the focus is centred) or internally generated (ie. from associations, when the features lead to memory recall without processing or emotional memory recall dependent on stored values). Neuronal firing representing the unconscious features is likely to fade if other features or events take priority, hence perceptual load of the conscious feature is increased, or there could be a shift towards the feature becoming conscious (ie. preconscious features) if for example, the memories or values recalled spontaneously deem the feature more important than the conscious ones. Conscious features of an event with top-down attention can be considered the highest level of cognitive processing. In this case, the features can evoke recall of memories with or without additional processing of the information since recall can be directed by the individual thinking or by the attended feature itself. Recall of emotional values, an active working memory with information processing, adaptation, categorising and problem solving can all occur with conscious information. However, the quantity of information that an individual is consciously aware of is limited. Just like with features being processed unconsciously, the neuronal firing can fade due to for example the task being completed, the individual losing interest or being distracted, other features of the event taking priority, or even that there is shift due to timing out of the neuronal trace from the refractory periods of the neurons themselves.

Top-down attention does not have to be focused on one single event, it can, as Marchetti in his article described, also be diffuse or distributed. This was described in Salt (2012) as normal, waking attention, where attention flits between features, taking in the gist of the event as a whole. Unconscious features are likely to be processed dependent on perceptual load limit and again, recall of memories and values associated with the features would be automatic and without processing or adaptation. The firing would naturally fade out, but interest due to the recall of associated memories or values could be enough to shift the attention to the focused top-down, higher attentional form. Even conscious awareness of features in this diffuse attentional state does not reach the same level of conscious processing of the focused attentional state since there is fast moving, fast changing flitting of attention without a full, in-depth representation of the event being realized. It is the state where a group impression is formed and memories and values are unconsciously and consciously steered.

A state not described in Marchetti`s article is the top-down attentional fear state. In this emotional state with this level of attention, lots of material is automatically processed unconsciously with subsequent memory and value recall because the limit of perceptual load in this state is bigger even though there is a loss of representation quality. Less detail is probably more significant at the conscious level rather than the unconscious one because it is more likely that an unconscious feature would be ignored completely. Again, neuronal firing of the unconscious feature representation would fade if the feature is not seen as a threat. Past experiences associated with the unconscious feature and the value attributed to it could however, shift the feature from unconscious to conscious. As stated above, with conscious awareness in the fear attentional state there is a perceptual load increase even if quality is sacrificed. Memories associated with the features can be recalled with or without  processing (recall may be directed by individual thinking or by features) and values, an active working memory with information processing, adaptation, associations and even problem solving if necessary are linked to this status. There also appears to be a ´slowing` of time which is attributed to the increased perceptual load and level of informational input.

However, top-down attentional state is not the only human attentional system – bottom-up attention also exists and this is probably Marchetti`s exogenous system with physiological and functional equivalents existing in other living things. In the bottom-up attentional state there can also be a focus on certain features. This means, just like with the top-down system, that these features have been in some way selected. Whereas, selection in the dominating top-down system is linked to memory and value recall, selection in the bottom-up system relates to the features` characteristics themselves, eg. colours, size, movement for visual features and this is determined by the sensory physiological system itself. Conscious input of such features themselves lead to memory and value recall, and can lead to a shift to top-down attention if thinking and processing is involved requiring the working memory system. The neuronal firing representation of the conscious feature can fade if, for example recognition occurs, or interest shifts to another feature, or naturally with time if the refractory period of the firing cells is reached. In this latter case, saccades occur which is where attention is drawn to other neighbouring event features giving the appropriate neuronal cells time to neurochemically recover. There can also be a shift up to top-down attention if the working memory becomes involved to process the information, or there is stimulation of internal thinking, eg. about future intentions. Just like with the top-down attentional system some features, however, undergo unconscious processing and these features may be peripheral to the main focus or less physiologically demanding. The unconscious features are automatically processed and memories and values recalled if perceptual load allows. Neuronal firing representing these non-conscious features will also fade if timed out by the non-firing refractory period of the neurons involved or may shift to other features if interest is lost or the other feature is competitively superior with regards to the physiological system in use. A shift up to conscious awareness is also possible, eg. if interest is awakened through the automatic recall of a stimulating memory linked to the feature.

The second bottom-up attentional state is where there is no focused attention, but instead attention is diffused or distributed. In this case, attention ´flits` and it is stimulus driven by the feature characteristics themselves and the physiological system involved, but attention is distributed so that the event is seen as a ´whole` with no single feature grabbing the focus. With unconscious processing, features can be individually processed according to perceptual load limits and the recall of memories and values are initiated automatically, but it is unlikely that processing is to any great depth. An exception to this would be the recall of distressing associations prompted by a stimulus which would immediately shift the feature to conscious processing and probably top-down attention. Under normal emotional conditions, in the case of any conscious awareness, the features would be processed, but only to a degree where there is automatic perception since the diffuse attention means that no single feature dominates. The event is treated as a ´group experience` and the features are bound together even if individually received and perceived. Neuronal firing of any feature fades as another takes over, but there can be a shift to bottom-up conscious processing if one feature dominates physiologically or even top-down attention and awareness if the automatic processing of one feature stimulates to such a degree that it becomes the centre of the event.

The final attentional state is the bottom-up fear attentional state which is probably one of the most important attentional states for survival and is likely to be seen in many living species. In this case, features which the individual is consciously aware of are fully processed according to the system`s perceptual load limits relating to quality and quantity (eg. higher quantity, lower quality). In this case, event features are inputted and the firing patterns occurring result in automatic recognition, recall of memories and values that induce the emotional fear state in the individual. Fading only occurs if the stimulus is not seen as a threat anymore, otherwise the firing continues buoyed by shifts of attention to other event stimuli (eg. eyes flitting around). Instigation of the working memory system to process the incoming information, eg. to find an escape route, assess the danger, engages the brain areas linked to top-down attention and therefore, shifts attention from the bottom-up level. In the fear attentional state, bottom-up unconscious processing can occur if the perceptual load limit in this state is not reached. If it does occur, features are unlikely to be processed fully unless perceived as a threat from the automatic memory recall instigated from the feature perception. Fading occurs if the stimulus is not deemed a threat, or if other features of the event take the focus and perceptual load allocation of resources is applied to the other feature.

By understanding when and where each form of attentional system comes into play and therefore, what information is likely to be attended to and what is non-attended to, we can manipulate conscious awareness and attention to our cognitive advantage. For example, fading in focused top-down attention, could occur through boredom, other events taking priority, or the natural timing out of the firing. Therefore, in this case fading can be prevented by for example pointing out other features of the same event, or introducing novelty, or providing a question to be answered. This will stop the focus and conscious awareness from being shifted to alternatives. The same methods could be applied to prevent fading of focused bottom-up attention. In the case of unconscious processing, for example fading will occur naturally since the individual is unaware of the input and processing being carried out. Natural fading can be prevented by shifting the event from being unconsciously processed to being consciously processed, eg. by evoking memories or values associated with features of the event, or by drawing the attention to particular features. These types of ´tricks` can be and are applied to daily life. For example, advertising uses flashing images, moving images, bright colours, centre-stage placing to bring focused bottom-up attention to their product and content is included that appeals to their market customers to elicit top-down attention eg. cute puppies, fast cars.

On a more important note, the knowledge about shifts to conscious and unconscious awareness can be applied to aid individuals suffering from memory or attention problems, eg. ADHD and Alzheimer`s disease. In the case of Alzheimer`s disease, Damasio in 1999 said that in its advanced stages, sufferers exhibited a dissociation between low-level attention and consciousness. In this state, the sufferers exhibited some basic signs of attention such as being capable of forming sensory images of objects and performing accurate movements relative to those images. However, they were incapable of employing any sense of Self by wishing, expressing past experiences and future intentions for example which is indicative of the higher-level attentional system and consciousness. Therefore, in this case, the presentation of objects from the individual`s past, or of the individual`s peer group`s past that are of value to the individual could stimulate unconscious processing and stimulate the recall of unconscious memories and values. Binding of new information to this recalled information may aid memory formation even if there is no conscious awareness of it. Other possibilities would be the use of peripheral vision and distributed attention to increase the volume of unconscious processing, the use of distracting stimuli requiring more eye movement, fleeting bright colour presentation and, although prohibited by ethical concerns the binding of new information to objects of fear such as fire or spiders. The difficulty comes for researchers in determining how much conscious awareness there is when reliable reporting by the sufferer is not possible. In this case, advanced neuroimaging techniques over a long period may help demonstrate the success or failure of the information intake.

Therefore, the interrelationship of attention and conscious awareness and the different physiology and mechanisms involved is an important topic and one that, no doubt, will keep our attention for many more years.

Since we`re talking about the topic …………………………..

…..can we assume that learning during sleep employs the same top-down and bottom up attentional processes as whilst awake, but quantity and quality may have different limits?

……is it possible that drugs that reduce focus or favour the diffused attentional state can be used to explore the limits of attention (ie. measurement of phi), the effects on peripheral vision, and inattentional blindness?

……could neuroimaging of the brain areas of minimally conscious individuals be carried out when they are presented with a range of smells (visual and sounds are also possible, but are likely to produce less specific images) so that knowledge about the basic attentional systems focused on single events can be expanded?


Posted in attention, consciousness, Uncategorized | Tagged ,

theta clock-spiking cells in the hippocampus

Posted comment on ´Theta rhythmic clock-like activity of single units in the mouse hippocampus` by L. Zhang, X. Ma, G. Chen, E. Barkai and L. Lin and published in Journal of Neuroscience 36(16) 2016 p.4415


In their article, Zhang and colleagues describe their finding of a small group of neurons in the mouse hippocampus that exhibit theta oscillations only during waking exploration and REM sleep. The cells were named theta clock-spiking cells and the theta oscillations exhibited by these cells differed to those oscillations normally found in the hippocampus as part of the information processing and memory functions for example.

Zhang and colleagues took 15 C57/6J freely behaving mice and used drivable microelectrode arrays to record the brain wave oscillations. Three 36-pin connector arrays were positioned in parallel and for the recording microdrive two independently movable electrode bundles of 12 tetrodes and 24 stereotrodes were implanted into both sides of the dorsal hippocampi. The connector pin arrays were connected to amplifiers and the extracellular signals were filtered through these amplifiers to separate neuronal activity and local field potentials (LFP).  Spike signals and the LFP signals were filtered on-line at different frequencies and spike waveforms, time stamps, and LFP signals were saved to Plexon data files. Individual neurons were identified and sorted by clustering methods using Offline Sorter version 2.8 software. Interneurons were identified by their greater than 5Hz firing rates and 100-300 μs spikes and interspike intervals (ISIs) were used to further isolate theta clock-spiking cells from the other cells. Oscillations were identified using bandpass filtering with theta oscillations LFP at 4–12 Hz, gamma 30–80 Hz and ripple oscillations 100–250 Hz. Stationary data was selected for further analysis using an augmented Dickey–Fuller test and power spectral density analyses were performed on both spike and LFP signals. Theta phase locking firing was analysed using a Hilbert transform to split into instantaneous amplitude and phase components, followed by spike phase and phase locking calculations. Animal behaviours during the testing process were digitally recorded and positions and firing rate maps constructed. Locomotion velocity and firing rates were also calculated.

Their investigation led Zhang and colleagues to identify a small subset of neurons (5 cells) in the stratum oriens region of the hippocampal CA1. The cells were named theta clock-spiking cells and they were found to only persistently fire during REM sleep and in the theta states of waking exploration. They were silent during slow-wave sleep. The theta oscillations exhibited by these theta clock-spiking cells had a simple clock-like spike firing pattern with one spike per theta cycle. There was a significant difference between the average firing rates of these cells and the peak frequencies of power spectra of corresponding LFP theta neurons and therefore, it was concluded that from the five cells, four bore no relationship to theta LFP. The authors calculated firing rates of the theta clock-spiking cells under different theta states and found that the average firing rate during waking exploration was about 9Hz which was slightly higher than the peak frequency of the power spectrum of the corresponding LFP theta rhythm in the hippocampus at 8Hz. The average firing rate during REM sleep was also found to be different for the 4 cells with the theta clock-spiking cells exhibiting a theta frequency of about 5Hz with other cells with the LFP theta rhythm having a slightly higher frequency of about 7Hz.

Zhang and colleagues also looked at other cells in the hippocampus CA1 area. Out of 508 cells, they found 44 theta locked interneurons, 30 theta unlocked interneurons in the stratum oriens and stratum pyramidale and 425 complex spiking cells in the stratum pyramidale. Therefore, their subset of theta clock-spiking cells was very small. The method of cell separation meant that these cells were together where neurons were sparsely distributed.  The authors also found ripple oscillations with these cells, but these were smaller than for cells of the stratum pyrimadale area and the theta oscillations had a delayed 3–24 ms phase, which indicated that the soma of the theta clock-spiking cells were probably located in the stratum oriens. The cells also showed a different peak interval of ISI distributions to the other theta oscillating cells of the area. A difference was found between the firing rates of the theta clock-spiking cells and the complex firing cells observed during wakeful exploration and also by differences in spike durations between the theta clock-spiking cells and interneurons.

An investigation into gamma oscillations led the authors to the conclusion that the theta clock-spiking cells were not locked to gamma oscillations in REM sleep nor wakeful exploration, or to ripple oscillations in SWS. The cells, although they showed non spatial preference, appeared however to be linked to locomotion velocity.

Therefore, Zhang and colleagues found in their study a very small subset of theta oscillating cells in the hippocampus CA1 which exhibited firing under two conditions; REM sleep and waking exploration. They hypothesized that these theta clock-spiking cells may provide a temporal reference in theta-related temporal coding or decoding of information in the hippocampal area, but unlike the place cells of the area, they did not encode spatial information. Owing to the correlation between the theta clock-spiking cells firing and locomotion velocity the authors hypothesized that there may be link between this small subset of cells and speed.


What makes this article interesting is firstly, the way in which a small population of cells can be detected and secondly, the complexity, both temporally and frequency-related, of brain waves. Accurate detection of small cell populations could lead to more precise attributes of function to brain area and could also lead to experimental and therapeutic methods where manipulation of small populations of cells only could lead to widespread functional and structural effects. Understanding brain waves and their functions in smaller cell populations could provide a means of manipulation, eg. by specific transcranial magnetism, that could result in widespread neuronal effects. Although, the main emphasis in Zhang and colleagues` study was the theta oscillation, their hypothesis could apply to all of the brain wave types.

Unfortunately, one of the problems with Zhang and colleagues experiments was the low sample number of theta clock-spiking cells found – only 5 cells out of 15 mice out of over 500 cells looked at and even one of the 5 was not the theta clock-spiking cell in question. Several reasons can be brought forward to explain such a low number of cells in an area known for its cognitive function. For example, the theta clock-spiking cells could represent anomalous readings. However, this is probably unlikely since theta oscillations were definitely different in the theta clock-spiking cell to the theta oscillations observed with other cells and those differences were observed over different situations, eg REM sleep and waking exploration. Support for such cells also comes from other species since clock spiking cells have also been reported in the optic lobes of insects as early as 1965. The small subset of cells identified by Zhang and colleagues could also represent immature cells or cells not at the same point of their life cycle relative to other theta oscillating cells in the area. This possibility is also unlikely since all theta clock-spiking cells were found in one area only, the stratum oriens, and the cells were not morphologically different to the other hippocampal cells. Another explanation is that the results represent cells active in a common behaviour which is not displayed by this specific mouse strain. This explanation could be considered possible since the hippocampus exhibits neurogenesis and cell function adapts to cope with the animal`s behavioural requirements. If this mouse strain is not very exploratory for example, maybe the number of cells responsible for this function is decreased relative to other mouse strains and therefore, a low number of cells would be observed. Similarly, the results could represent cells active with a very specific function. This is another possible explanation although in this case this specific function must be in low demand in this mouse strain or in the day-to-day life of these mice.

Therefore, since we assume that the small population of theta oscillating cells identified by Zhang and colleagues is functionally and detectably different to the other theta oscillating cells of the hippocampus it is necessary to determine why they are there. Zhang and colleagues investigated whether the firing pattern of these theta  clock-spiking cells could contribute to the hippocampal self-generated theta oscillations in general since several intrinsic, atropine-resistant (ie. not cholinergic cells) theta generators have been found in the CA1 using isolated rat hippocampal preparations. However, since the  author`s experiments showed that the theta clock-spiking cells demonstrated a different frequency of firing rate from the peak frequency of the theta oscillating cells contributing to the local field potential, their involvement in cognitive functioning such as information processing and memory from a content point of view was deemed unlikely. However, Zhang and colleagues did speculate that the activity of the theta clock-spiking cells may provide an overall time reference for the theta phase precession of intracellular membrane potential oscillations in place cells. Therefore, the theta clock spiking cells could play a temporal role. The authors also did find a specific function linked to the small subset of cells since there were correlations between the firing of the cells and the locomotive velocity of the animals during waking exploration. It was concluded then that there could be a link between theta oscillations and speed and this has been reported in other studies as well.

So, how can we explain theta oscillations, locomotive speed and the hippocampus? In this case of being awake and exhibiting exploratory behaviour, there are two sources of inputted information: visual speed (ie. the change in visual information inputted of the environment while the animal is exploring) and ´run` speed (ie. the speed of mouse movement). Previous research has shown that input of both occurs via the V1 visual cortex, with the input and interpretation of the information involving the hippocampus. The hippocampus is known to be strongly correlated to cognitive functions such as memory and spatial navigation, both required in exploratory behaviour and there is a link between sensory input (visual information in visual cortex) and object and location of spatial information in the hippocampal place cells during waking exploration. This link is not only demonstrated at the cellular mechanical level, but also through brain waves. Brain waves represent synchronous firing of cells and the frequency of the brain wave demonstrates the speed of neuronal firing at that time. By measuring the brain waves of one area or between areas, functioning and connectivity can be observed. For example, firing between the thalamus and cortex is activated in a specific temporal sequence and this connectivity can be modulated by inhibiting the inputs from the thalamus reticular nucleus which is GABA dependent. Another example involves the prefrontal cortex which is also important in memory recall and is linked to increased theta oscillations in temporal order maintenance whereas alpha oscillations are required for item maintenance. The hippocampal theta bursts drive the generation of prefrontal cortical theta-gamma dependent hippocampus coupling and firing of the enterorhinal cortex. Theta oscillations are also linked to memory and in the case of waking exploration, the mouse uses its spatial memory for interpretation as well as storage of information for future use. Formation of new memories involving the CA1 neurons occurs with encoding at pyramidal cells preferentially timed later than the theta oscillation peak coincident with input from the enterorhinal cortex and retrieval of memories occurring at the theta oscillatory trough coincident with firing input from the CA3 region. Lesions of the enterorhinal cortex lead to disruption of these theta oscillations and silencing of the CA3 neurons resulting in loss of temporal coding in the CA1. However, the authors demonstrated in their experiments that the theta oscillations observed from their theta clock-spiking cells were different to those of the pyramidal cells and therefore, it is unlikely that these cells are directly responsible for information processing and memory formation of the event. However, it is possible that the theta- clock spiking cells although not directly responsible for the content of the event during the active times of the waking exploration as is the normal function of the theta oscillations, provide instead a ´background pulse` for times of intervals in the exploratory behaviour, ie. akin to a drum beating time. Spontaneous firings of the 4 cells would keep the area in a state of ´readiness` whilst active place cells undergo the biochemical refractory periods necessary during continual firing periods. This is seen with saccades in retinal cells and incoming visual information. Refractory periods of the active visual cells means that priority of event characteristics is shifted to the unattended features and there is temporary inhibition of return so that the cells representing the important event characteristics can biochemically recover ready for the next wave of firing.

This explanation could also apply to the other scenario where theta clock-spiking cells are observed, that is in REM sleep. In this case, the mouse undergoes no exploratory behaviour, but is motionless with no visual input and therefore, functioning of this particular sub-group of cells cannot be contributed to visual speed and run speed, or place cell activity recording object and location. However, just like in the waking exploration scenario, in REM sleep there is predominately another brain wave frequency representing informational content and manipulation. In this case, the frequency of the brain wave activity is beta with interspersed low frequency theta oscillations. The function of the brain waves here is just like in the waking case, to represent synchronous firing of groups of cells, but in the case of REM sleep, the firing is linked to memory formation and consolidation. This function is supported by the observation that REM sleep is disrupted by inducing sleep apnoea and this leads to significant negative effects on spatial navigational memory. Therefore, what function could Zhang and colleagues population of theta clock-spiking cells have in REM sleep? Just like in waking exploration, this subset of cells could be the ´default` cell, providing the ´background pulse`, essentially active when the firing cells representing the event features during this memory formation period reach their refractory periods. In REM sleep, the frequency of the normal oscillatory rhythm for memory formation and consolidation is also beta with spiking theta rhythms. This combination of primary frequency and secondary frequency can also be observed under other circumstances. In NREM stage 2 sleep there are sleep spindles observed with theta oscillating cells as spikes, or in slow wave sleep there are bursts of sleep spindles where new information is being integrated, replay is seen and there is reconsolidation of memories. Therefore, like a temporal marker, the theta clock-spiking cells in REM sleep could be ´innate` markers spontaneously firing to maintain area ´readiness` whilst other cells biochemically recover from firing. This hypothesis is supported by the observation by Bernardi that sleep deprivation, known to be linked to poor memory recall, leads to region specific increases in theta oscillations suggesting that theta oscillations represent transient neuronal states unrelated to event content.

Therefore, this article is interesting because it demonstrates just how complicated neuronal firing is and how monitoring of brain wave functioning has to be carried out on much smaller scales if we are to determine how cell firing is linked to information processing and memory. It could be that the theta clock-spiking cells identified by Zhang and colleagues are just ´artifacts` or a spurious observation of a few hippocampal cells, but they could be the ´default` firing cells of this important area keeping it in ´readiness` whilst other cells biochemically recover. Input and binding of information and interplay between the hippocampus, enterorhinal cortex and cortical areas may focus research attention on the predominant brain waves and cell firing during event characteristic registration, but if the theta clock-spiking cells are linked to the ´default` state of the hippocampus then disruption of their functioning may prevent correct informational input and interpretation overall. For example, Alzheimer disease is linked to hyperexcitability of the hippocampal region and there are currently no hypotheses on how this comes about. It could be that the fault lies not with the neuronal cells involved with informational input, but with cells like the theta clock-spiking cells who are involved in the correcting functioning of the area, but who are not event related. Therefore, investigation of small groups of cells is important.

Since we`re talking about the topic……………………………….

…..can we assume that if the theta clock-spiking cells are linked to a specific activity which is not in much demand during waking exploration, if the mice were trained to  perform a task dependent on mouse speed, then we should see a major increase in number of this subset of cells if their function is truly linked to locomotive speed?

…… since cannabinoids disrupt theta oscillations in the hippocampus and ketamine increases theta oscillations in the medial frontal cortex, if Zhang and colleagues` experiments were performed again with these drugs pre-administered would we see how the theta clock-spiking cells are linked to normal brain wave functioning in these areas?

……can we assume that if the mouse is exposed to anaesthetics and brain waves are monitored we would see more and more of the brain going into slow wave oscillations as normal, but we would see an effect on the theta clock-spiking group of cells?

…….sleep deprivation leads to region specific homeostatic increases in theta oscillatory activity and therefore, would there be an increased number of theta clock-spiking cells if these cells are linked to the ´default` firing state of the hippocampus?

…….is it possible that the GABA agonist, zolpidem, which leads to increased sleep spindles and increased REM would produce noticeable effects on the number of the theta clock-spiking cells?


Posted in brain waves, hippocampus, neuronal firing, Uncategorized | Tagged , ,

neuronal firing patterns with true and false memories

Posted comment on ´Neural Global Pattern Similarity Underlies True and False Memories` by Z. Ye, B. Zhu, L. Zhuang, Z. Lu, C. Chen and G. Xue and published in Journal of Neuroscience 22nd June 2016, 36 (25) 6792-6802; DOI:


Ye and colleagues used as a basis in their investigation of neuronal firing of memories the  computational global matching model that hypothesizes that memory strength of a given item is derived from the match (measured as similarity) between its representation and all other studied items (i.e memories) at that time. They looked at the strengths of true and false memories that arose from global similarity of each item`s neural activation pattern during retrieval to that of the groups during encoding and called it ER-nGPS.

In their experiments, Ye and colleagues used fMRI and the participants were scanned during both the encoding and retrieval phases of the memory task, which was an adapted version of the Deese-Roediger-McDermott (DRM) paradigm. The participants, who were 35 healthy college students, were presented visually with 9 word lists each containing 12 words that related to one particular theme. Eight of the 12 words were part of the group study and the other 4 were used as ´critical lures` (words encoded, but not presented in the recall part of the test). Thirty-six semantically unrelated words were also used as ´foils` (non-studied words) in the recognition test.

For the encoding phase, each word was presented for 3 seconds. Then, the participant was asked to perform a perceptual orientation task for 8 secs to prevent further processing of the recently presented word. They were asked to judge each word as pleasant or not and to give each a value from 1-4 by pressing a button. In the retrieval phase, the participants were first given a 2-back working memory task for 10 minutes as a distraction. Then, they were asked on the presentation of each word (36 studied, 36 critical lures and 36 foils) if they judged the word being presented as being definitely new (given a rating of 1) to definitely old (4). These confidence responses rated memory strength. The similarity of the global neuronal firing patterns between encoding and retrieval of the test items was assessed for all 72 studied items.

The participants were also asked to rate semantic similarity. For each task given, they had to assess pairwise semantic similarity to the tested items. In this case only 8 words and 4 critical lures from each word list were used. Judgement for semantic similarity between the two words was tested by rating using a value of 1 for a very weak semantic association to 6 for a very strong semantic association.

The information obtained from the experiments underwent behavioural and univariate activation analyses. In the behavioural analysis, the differences in the endorsement rates of target, lure, and foil items and associated reaction times in the recognition test were examined.

In the univariate activation analysis, single-item response estimation, neural global pattern similarity between encoding and retrieval (ER-nGPS), ROI analysis, mixed-effects model and mediation analysis were carried out.

Ye and colleagues found that in their behavioural tests there was a mean endorsement rate of 90% for targets, 46% for lures, and 11% for foils showing that the participants exhibited high accuracy for true memories, but also showed a high level of false memories. In the rating of items, the authors found that for the targets and lures that the memory strengths of the studied items related to semantic similarity. Target items exhibited high memory strength and semantic similarity was higher for lures than the targets.

For their fMRI-based results, Ye and colleagues used the calculated ER-nGPSs for the individual items which were the neural activation pattern similarities between each item during its retrieval with all other items during their encoding. They examined whether the ER-nGPS was associated with memory strength and found agreement. There was high memory strength in high ER-nGPS areas such as left inferior frontal gyrus (LIFG), left inferior parietal lobule (LIPL), left superior parietal lobule (LSPL), and left ventral lateral occipital complex (LvLOC). This was confirmed by linear mixed-model analysis where increased true memory strength increased the ER-nGPS observed. However, not all categories produced the same results. Therefore, the authors tested the lures, but not the foils because they showed very low memory strength. In the case of the lures, the ER-nGPS increased with memory strength in the LIFG, LIPL, and the LSPL, but not in the LvLOC or right ventral lateral occipital complex (RvLOC).  Within the word lists similarities produced similar results. Therefore, the authors concluded that the ER-nGPS of the frontoparietal regions was associated with the strength of both true and false memories, whereas the ER-nGPS in the visual cortex was only associated with the strength of a true memory.

Ye and colleagues also investigated the activity in the medial temporal lobe which is associated with memory. In these experiments they used whole brain searchlight analysis and found that the ER-nGPS was not associated with memory strength. Four regions of interest (ROI) were identified and they found that there was only a slight significant difference between a high memory strength item and one of low strength in the left hippocampus only.

In their experiments on sematic similarity, the ER-nGPS reflected the similarity and hence mediated the effect of semantic global ratings (sGS) on memory strength. They found the similarity in the LSPL, partially in the LIPL, but not in the LIFG and therefore, concluded that ER-nGPS is more sensitive to the content of episodic representation rather than univariate activation level.

In their experiments to investigate if ER-nGPS could differentiate between true and false memories, Ye and colleagues looked at the area which exhibited high strength for the lures, ie. the RvLOC.  In this area they found greater activation for targets judged as old than lures judged as old.  Two other areas showed the same results:  the right intracalcarine cortex extending to the right lingual gyrus and a small cluster in the right superior parietal lobe. When the authors looked at activations for correctly rejected lures and foils judged as new, they found strong activation in a large cluster of the left lateral prefrontal cortex (responsible for cognitive control and conflict resolution) and in a small cluster in the medial frontal cortex (responsible for conflict processing). They also found stronger activation for the group of  targets judged as old than that of  foils judged as new in the left lateral prefrontal cortex (LPFC), but found no difference between lures judged as old and lures judged as new,  or between targets judged as old and lures judged as old. Therefore, their experiments recorded strong positive association between the activation of LPFC and ER-nGPS in the LIPL for both true and false (ie. lure) items. This led to the authors concluding that the activation of the LPFC was associated with the discrepancy of the ER-nGPS in the LIPL and the visual cortex. When a mixed-effect model analysis was carried out, a strong positive association between the ER-nGPS difference (LIPL − RvLOC) and the left LPFC activation was found. Hence, it was concluded that the cognitive control process might result from a discrepancy between the ER-nGPS in the parietal and visual cortices.

The univariate activation level experiments in different brain areas also reflected the activation levels determined from the fMRI analyses. The authors used a mixed-effect regression model and found that after controlling for univariate activation levels, ER-nGPSs were still a significant predictor of true memory strength in all ROIs such as the LIFG, LIPL, LSPL, and LvROC and were a significant predictor for the strength of false memory in the LIPL and a marginally significant predictor in the LIFG. They also found using univariate analysis that there was greater activation for true memory than false memory in the left MFG, bilateral IPL, precuneus, and anterior and posterior cingulate cortices. However, these regions did not overlap with those showing differences in ER-nGPS between true and false memories.

In summary, Ye and colleagues showed in their experiments based on global matching computational model that memory strength of a given item depended on how it was encoded during learning and on its similarity of its neural activity pattern with other studied items. They showed multiple ER-nGPSs carried distinct information and contributed differentially to true and false memories. The location of the ER-nGPS was also found to be important. Parietal regions reflected semantic similarity and ER-nGPSs were scaled to the recognition strengths of both true and false memories, ie. to studied and unstudied items whereas activity in the visual cortex areas contributed solely to true memory. The differences between parietal and visual cortices correlated to frontal monitoring processes. Therefore, it was concluded that multiple neural mechanisms underlie memory strengths of events registered in the brain and this area requires further research and discussion.


Ye and colleagues looked at the neuronal activation patterns measured using fMRI that are associated with real, experienced objects (here, words) and correlated these to whether the participant has seen the word before (termed here a ´true` memory), or not (a ´false` memory) and to the degree of similarity the presented word had to others. They interpreted their results using mathematical modelling and statistical analysis. What makes this article interesting is first, that neuronal activation relating to encoding and retrieval is put on a mathematical modelling basis, and second how this type of measurement gives information about the neurochemical mechanisms involved in how objects are learnt and recalled. The experiments show that instead of researchers looking at neuronal activation patterns for single items, they can actually look at activation patterns for whole groups and see the difference when one member of that group is removed. Essentially, this is what is being used to determine where the default mode network of consciousness lies. Consciousness researchers take a neuronal activation picture of a conscious experience and keep removing the activity from the area with the lowest activation until only one area is left. This was said to be the root of the conscious experience. Ye`s experiments also confirm that activation patterns are strengthened by grouping items and that overall activity reflects the source of the stimulation as well as its actual content. This can be said since if only the content is reflected in the neuronal trace then there would be no discrepancy between the patterns achieved between the imagined or false words and the real images observed in past (ie. true).

So, how can the experiments be explained from the perspective of neurochemical mechanisms? The experiments begin with the encoding of the word lists. When the first word is shown to the participant, the visual pathways are stimulated leading to the activation of the sensory stores and then short term memory stores. The brain`s linguistic centres are activated since the word is recognized because all words used were known. Learning is achieved by repetition as the participant is given 3 seconds to commit the word to memory, essentially a reasonable time in learning terms. This results in the neurochemical mechanisms being activated for long term storage. (Long-term storage is assumed because recall takes over 10 minutes later.)

The first word presented can be said to be learnt ´pure`, ie. without processing because there is nothing to relate it to. Even if processing occurs it is likely to be at a low level and of a general nature inspired because of how the participant knows he will be tested in the future, ie. with the association of meaning. Learning is reinforced by the given task of rating the word according to ´pleasantness`. It is known that assigning an emotional value (the ´emotional tag`) to a memory can affect learning and its later retrieval. In Ye and colleagues experiment, learning is also reinforced by the multi-modality of the task, that is the participant sees the word (visual sense) and then must press a button to give the word the emotional value. Hence, different areas representing the visuomotor areas are activated and all added to the overall pattern of firing. The activity of these areas, however, remain the same for all the words, since only the visual information, emotional value and meaning of the word are different.

The presentation of the second word of the word list instigates essentially the same systems as for the first, ie. the same visual pathway, attentional, memory and motor mechanisms. Apart from the individual characteristics of the word, the presentation of the second word differs because of the level of processing carried out by the participant.  It is known that the second word has an apparent link to the first and this link is not visual, but in word meaning. The participant knows that the word belongs to a group and it is likely the word is processed unconsciously since the words used are familiar. This accounts for the speed of whole process. The type of processing carried out is categorization and therefore, the psychologist models of relatedness and schema likely come into play. Accretion probably also applies which is where the addition of a new example to relevant information already in memory (in this case, the previously learnt words) leads to tuning and restructuring if necessary so the schema is more accurate. Inferences could also be used.

It is known that processing and linking to other words results in stronger neuronal cell patterns. The presentation of visual information leads to known patterns of connectivity in neuronal cell firing. Guidotti found that spontaneous brain activity could be evoked by previously presented stimuli. Task evoked patterns to trained stimuli versus novel found patterns in several cortical regions such as the visual cortex, V3, V3A, V7, DFN, precuneus, inferior parietal lobe, dorsal attentional network (intraparietal sulcus which discriminated between trained and novel stimulus).  This agrees with areas demonstrating brain activity in categorization such as the V1, basal ganglia and bilateral intraparietal sulcus as shown by Seger. In Ye and colleagues experiments they found activity in areas such as the LIFG (an area associated with speech comprehension), LIPL (language, sensory motor control of writing – here probably the button pushing), LSPL (spatial orientation, sensory information from the hand – normally writing,  but here again the button pushing) and LvLOC (associated with the visual process). Parietal lobe activity is also associated with attention, the visual perception-action WHERE model which fits in with word recognition and the required motor processes and working memory with the inferior area associated with multi-modality and the lateral inferior being highly sensitive to memory learning recency, but not repetition. According to theories on the neurochemical mechanisms linked with object recognition, activity in the medial temporal lobe is associated with encoding success and so this V5 area is linked to form, sleep, movement, visual perception, and visual working memory. Therefore, Ye and colleagues results of no activity in this particular area was a surprise.

Again repetition aids the learning process of the second presented word and the whole procedure is repeated until all the words in the list are learnt. In the recall part of the test, on presentation of a word the participant asks himself if the word has been seen before and hence, was one learnt in the previous stage. This is an easier test of recall than one of asking the participant to remember each word presented. It is unlikely that a participant could recall all 12 words from the beginning as a list because he would have needed to have employed mnemonic methods in the learning phase and that is not likely considering the time frame of presentation and learning and the distraction task. Instead, the category is sought out and the features that make up that category and the participant has to rate the level of certainty about whether he has seen this word previously, or not. Jang provides a neurochemical explanation for this as it was shown that the  brain encodes experience in an integrative fashion by binding together various features of an event into what was termed an ´event file`. A subsequent reoccurrence of an event feature could then cue the retrieval of the memory file to ´prime` cognition and action. The ´event file` could also include attentional control states, emotional values etc. It was found that areas such as the hippocampus and putamen integrate event features across all these levels in conjunction with other regions representing concrete-feature-selective (primarily visual cortex) and category selective (posterior frontal cortex) and control demand selective (insula, caudate, anterior cingulate, parietal cortex) event information. Hence, according to Jahnke words are learnt as a group and the retrieval of one would mutually generate and support the rest of group. This was seen with sharp-wave ripple complexes (short episodes of increased activity with superimposed high frequency oscillations) occurring during rest and sleep which showed that replay and the SW were tightly interconnected. Such activity was attributed to dendritic sodium spikes found in the hippocampal CA3 and CA1 areas. Recognition of objects is also associated with activity in the perirhinal cortex (Malkora) and Ho showed that this area had a well-established role in familiarity based recognition of individual items. The area responds to novelty and familiarity by increasing or decreasing firing rates. Oscillatory activity occurs in the low beta and low gamma frequency bands in sensory detection, perception and recognition. Stimulation of this area at 30-40HZ causes old items to be treated as novel.

In Ye and colleagues experiments, presentation of each word in the recall part of the experiment requires the participant to make a decision of whether he has seen this word before, or not and perform a motor action. Therefore, neuronal traces also show activity in those areas involved in decision making, ie. the strength of the cortico-striatal pathway and prefrontal cortex (Daw, Chung-Chuan), parietal cortex (guidance of eye movements), basal ganglia, motor structures. This activation, just like those areas representing the visuomotor mechanisms of the button pushing, is the same whether the item has been encoded or not. Therefore, Ye and colleagues could conclude that the only difference between the neuronal traces observed, apart from visual attributes and meaning, was whether the word had been visually seen during encoding or not. Activity in the occipital cortex was observed for words presented during the encoding part of the test (´true` memories), whereas its absence denoted ´false` memories. This expands Fuentemilia`s observations that ´true` memories cause activity in the inferior longitudinal fascile (a major connective pathway of the medial temporal lobe), whereas ´false` memory relates to activity in the superior longitudinal fascile. In this case, the so-called ´false` items relate to visual imagery with the firing of multiple common features including general meaning, but not all are correct. Therefore, the task given is more difficult to get 100% correct and this was proven by the high number of false calls. Brascamp explains this by saying that when an individual knows he is faced with inconclusive or conflicting perception then there is a dominance of whatever perceptual interpretation was commonly reported on a previous encounter. In this case, by asking if a word had been seen before, the brain processed it as relating to the ´meaning` and the likelihood that it had been. Therefore, the word was deemed as being familiar. In order to achieve higher scores, the participant would need to divorce this feeling of familiarity with recognition of the word in a visual capacity only.

In Ye and colleagues experiment, there was no official feedback as to the level of correct or incorrect answers given either instantaneously or at a later date and therefore, with the former, no real-time feedback processing. The participants may have intuitively felt that an error had been made and firing activity would then be visible in the anterior cingulate cortices and amygdala areas. However, again likelihood of activity in these areas would be the same for all words and would enhance the overall neuronal activation pattern rather than be present and specific for either real or imagined word groups.

Therefore, it can be concluded that Ye and colleagues experiments show that neuronal activation relating to encoding and retrieval can be put on a mathematical modelling basis and activation is better if whole groups are considered with the required single object being removed from this group rather than just looking at the activation pattern of the object on its own. This type of measurement shows that retrieval appears to be improved when an object is learnt with its meaning and with others of the same category and not by word structure, or by order. This observation could lead to new learning techniques in the case of reminders for example which are important in prospective memory or for those suffering from forgetfulness.

Since we`re talking about the topic……………………

……does emotional attachment to words change global activation patterns? Does the level of anxiety shown by a participant during the test change the activity patterns during encoding and retrieval and does it change the categorization of the words?

…….would instigation of instant feedback during retrieval, eg. giving a positive or negative visual sign change the speed or accuracy of the following replies and change the global activation patterns achieved?

……would a concurrent testing of brain waves within the parietal cortex, perirhinal cortex and hippocampus show the theta, gamma brain wave synchronicity and would these change during the course of test or by presentation of encoded or novel words?

…. would tests with patients with ventral medial prefrontal cortical lesions show that the number of false results is decreased compared to the control group since this brain area is linked to increasing the influence of schematically congruent memories (Warren)?

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astrocytic calcium ion surges and tDCS

Posted comment on ´Calcium imaging reveals glial involvement in transcranial direct current stimulation-induced plasticity in mouse brain` by H. Monai, M. Ohkura, M. Tanaka, Y. Oe, A. Konno, H. Hirai, K. Mikoshiba, S. Itohara, J. Nakai, Y. Iwai and H. Hirase and published on 22nd March 2016 in Nature Communications 7:11100 doi: 10.1038/ncomms11100 (2016).


In their article, Monai and colleagues discuss a possible neurochemical mechanism involved in transcranial direct current stimulation (tDCS), which has been described for example as helpful in alleviating depression and enhancing learning. Work by others using in vitro brain slices has shown that NMDA receptors are likely to play a role in the mechanism, which in general is unclear. One possibility put forward by Monai`s group and others is that astrocytes are involved in the NMDA receptor plasticity and the mechanism includes activation of the astrocytes resulting in rising intracellular calcium levels which may lead to the secretion of signaling molecules into the synapse. This release eventually leads to glutamate receptor plasticity at the post-synaptic membrane. Hence, Monai and colleagues investigated whether tDCS produces its therapeutic effects by causing astrocytic activation.

In their study, Monai and team used transgenic mice (mouse line G7NG817) which expressed G-CaMP7 (a green fluorescent protein Ca2+ indicator protein) based in the astrocytes and a subpopulation of excitatory neurons. They found high level of expression of the fluorescent protein in the astrocytes of the cortex, hippocampus (particularly the CA3 region), thalamus and striatum. Expression of G-CaMP7 was also observed in the neurons, but not in the cortical GABA cells. A level of tDCS of 0.1 mA direct current for 10 min induced large amplitude calcium ion surges in the astrocytes of the cortex. These surges exhibited higher amplitudes than spontaneous calcium ion events, but led to no changes in local field potential. There was also no change in local travelling wave propagation. Similar results were obtained for the anodal, contra-anodal and distal regions of the cortex. Long calcium ion surges were found to be more frequent during tDCS. The amplitude changes observed were the same for awake and anaesthetized mice, but in the latter onset of the surge was found to be more variable. In awake mice, the surges began several seconds after the tDCS onset whereas the calcium ion surges were found to be of a lower frequency in anaesthetized mice.

Monai and colleagues found that the t-DCS induced calcium surges in awake mice could be blocked by the administration of an alpha 1 adrenergic receptor inhibitor, prazosin, or by destroying the noradrenergic innervation with DSP-4. The surges were also blocked in vivo by local application of prazosin. Increases in astrocytic calcium ions were also not observed in IP3R2 (inositol triphosphate receptor type 2) knock-out mice and therefore, it was concluded that astrocytic GPCR activation is the prevalent mechanism of tDCS-induced Ca2+ surges. This view was further supported by behavioural investigations. Transcranial DCS was sufficient to alleviate a mouse model of depression by chronic restraint stress, but could not produce the same therapeutic effect after prazosin administration, DSP-4 treatment or IP3R2 deficiency.

To prove that the calcium surges were linked to astrocytes and not neurons, Monai and colleagues used two-photon imaging in layer 2/3 of the primary visual cortex. These experiments showed that the calcium surges were linked to SR101-positive astrocytes. Transcranial DCS evoked astrocytic calcium ion responses which had significantly higher amplitudes than spontaneous events, whereas the neuronal calcium ion events during tDCS had similar amplitudes to spontaneous events. The astrocytic calcium surges also occurred nearly seven times more frequently during the course of tDCS (10 min) than during the baseline, while neuronal activity did not show any obvious changes. The authors also used transgenic mice to confirm that the astrocytes were responsible for the calcium surge. They used transgenic mice with expressed G-CAMP7 in neurons or astrocytes using cell-type-specific recombinant adeno-associated viruses (AAV2.1-hSyn1-G-CaMP7 and AAV9-hGFAP-G-CaMP7, respectively) in C57BL/6 mice. Astrocytic soma, but not neuronal soma, were found to give rise to the long-lasting calcium ion surges associated with tDCS.

Monai and colleagues also investigated the role of NMDA receptors in calcium surges. They looked at visual evoked potential (VEP) of primary visual cortex of anaesthetized C57BL/6 mice after a flash stimulation of 60 secs before and after tDCS. It was found that the VEP slope increased by 50% after tDCS and remained at this increased level for at least 2hours after application. The effect was blocked by AP-5, an NMDA receptor antagonist and topical application of the alpha 1 adrenergic receptor antagonist, prazosin, but was not affected by the application of the muscarinic receptor, atropine. This indicated that NMDA receptors and alpha 1 adrenergic receptors play roles in the calcium surges induced with tDCS. There was also no VEP slope enhancement in IP3R2 knock out mice. Therefore, it was concluded that astrocytic calcium ion rises were involved in the tDCS enhancement of the VEP effect.

Therefore, Monai and colleagues concluded that tDCS induced astrocytic activity brings about plasticity changes in the cortex through calcium ion and Ip3 signalling. They showed that the tDCS-induced enhancement of a sensory evoked response is NMDAR dependent and as astrocyte calcium ion levels are positively related to the extracellular level of the NMDAR co-agonist d-serine, tDCS-induced astrocytic Ca2+ elevations possibly lead to NMDAR-dependent synaptic plasticity. The team also concluded that tDCS induced plasticity could be blocked by prazosin or DSP-4 treatment, thus indicating an involvement of alpha 1 adrenergic receptors which induce the G signaling cascade for IP3 production. The authors quote in their conclusion supporting work by Panktratov and Lalo who showed that application of noradrenaline raised extracellular d-serine and ATP levels and lowers the threshold for LTP induced plasticity in mouse cortical slices. They also conclude that the activation of A1AR is the prevalent mechanism for astrocytic calcium ion elevation in awake mice and suggested that the tDCS induced noradrenergic drive includes activation of the locus coeruleus and/or direct induction of transmitter release from noradrenegeric axon terminals in the cortex.


What makes this article interesting is that glial cells are again found to be more than just support cells for the all-important neuron and neuronal synapse. We know that different types of glial cells are important for different functions, eg. microglia are important in the degeneration of neuronal cells and oligodendrocytes are important in myelin production. This article focuses on another type of glial cell, the astrocyte, and expands its function in the action potential recovery phase and later on in the development of neuronal plasticity. The authors in their study showed that not only do astrocytes ´mop up` excess released neurotransmitters, binding of these neurotransmitters can cause intracellular astrocytic calcium surges that have an influence ultimately on neuronal NMDA dependent plasticity. This action can occur even spontaneously and hence, the excitable nature of the astrocytic glial cell provides yet another aspect to neuronal function that could go dysfunction and evoke deleterious changes in the neuronal and synaptic area. This additional role of the astrocyte has provoked lots of discussion simply because of the discrepancies in reported results, which have primarily arisen from experimenters using different brain areas, in vivo and in vitro samples and various experimental conditions.

Monai and colleagues have shown in their experiments that astrocytes produce internal calcium ion surges on binding of neurotransmitters to G protein linked receptors on the glial cell membrane. Using this finding, their aim was to show that these same calcium surges and resulting events could be produced by applying transcranial direct current stimulation (tDCS) and hence, one mechanism (or even, the mechanism) that is involved in the tDCS effect would be elucidated. Their finding supports observations about tDCS that it is effective in alleviating neuropsychiatric and neurological conditions such as depression in humans, and enhances learning and memory formation. Previous work on cortical slices shows that tDCS increases the excitability of the motor cortex in a NMDA receptor dependent manner, but the mechanisms involved in vivo are largely unknown with the exception that astrocytic calcium ion/IP3 signalling appears to play a significant role in synaptic plasticity in the cortex and hippocampus. Monai and colleagues` investigation confirmed this and showed that the tDCS induced plasticity was NMDA receptor dependent. They also found that the mechanism involved alpha 1 noradrenergic receptors which they believed transduced the G protein signaling cascade for IP3 production and was linked to the resulting calcium surges. This noradrenergic drive involved activation of the locus coeruleus area and/or direct induction of transmitter release from noradrenergic axon terminals in the cortex.

However, since there are discrepancies between the findings relating to astrocytic action it is not possible to say definitively what is going on in the case of tDCS. So, what do we know about astrocytic function? We know that astrocytes are one type of glial cell with the others being oligodendrites for myelination, microglia (shape shifters) as scavengers removing dead and damaged tissue from the nervous system, nerve/glial antigen 2 (NG2)-positive glia, which include oligodendrocyte and astrocyte progenitor cells as well as NG2+ cells that persist in the mature brain and astrocytes. It is likely that astrocytic cells share many of the characteristics and mechanisms of neurons. In fact, Liu showed that a single transcriptions factor, ACII, can convert astrocytes into functional neurons. Astrocytes are part of the action potential/firing stage of an active cell networking system and they can influence whether a neurite grows or retracts and regulate the content of extracellular space eg. they surround the synapse, remove excess neurotransmitter and control potassium ion concentration after the action potential has occurred, or in times of neuronal stress.

Astrocytes are capable of such actions by their physiology. The most common type are protoplasmic astrocytes and these cells have a very complex morphology and contact most, if not all, other cell types in the brain. The cells form from their soma (diameter 7-9 μm) elaborate and dense, fine non-overlapping processes that interact closely with the synapses present and supporting blood vessels (greater than 99% of the cerebrovascular surface is sheathed by astrocyte processes). It has been said that processes from a single astrocyte can envelop approximately 140,000 synapses which means that one astrocytic cell can occupy a ´working` volume of approximately 66,000 cubic μm. The cells are linked with each other by gap junctions and patch clamping experiments with a gap-junction permeable dye show that a single astrocyte rapidly leads to the filling of hundreds, even thousands of other astrocytes. Therefore, astrocytes likely function as a ´syncytium` contacting essentially all other cellular elements in the brain, including neurons, oligodendrocytes, NG2+ cells, microglia, and blood vessels.

In addition to this abundabt communication, there is also diversity within individual astrocytes with respect to interactions with the local environment. For example, it is possible that within a single astrocyte, a subset of processes (microdomains) can interact autonomously with neuronal synapses within its immediate environment while other regions of the same astrocyte interact with different groups of synapses or with other synaptic and neuronal elements such as blood vessels. These microdomains may not communicate with each other which implies that one astrocytic cell can carry out multiple functions simultaneously. However, this is unclear at this time and requires further investigation.

Known astrocytic functions appear to be linked to two mechanisms. The first is that described by Monai and colleagues that of glial cell surface receptor binding and calcium surges. Astrocytes are assumed to be like other glial cell types in that they have many signaling proteins similar to those found in neurons, eg. ion channels and receptors such as those for glutamate, GABA and noradrenaline. These specific neurotransmitter receptors are on the cell membranes and can trigger events within the glial cells and Monai and colleagues reported this in the case of noradrenergic receptors. This finding confirmed work from the mid 1990s which showed that activation of G protein-coupled receptors on the astrocytic cell membrane surface by synaptically released neurotransmitters produces rises in intracellular calcium concentration. An IP3 signalling cascade is involved and this demonstrates not only that astrocytes display a form of excitability like the neuron, but also that astrocytes may be active participants in brain information processing. The ultimate consequence of such a binding is the increase in NMDA receptor plasticity on the neuronal post-synaptic membrane which will affect overall neuronal area functioning.

Although the authors and others report a link to NMDA receptor plasticity, some researchers (eg. Goldman) report instead an increase in AMPA receptor trafficking and increased plasticity of the neuronal area. Goldman found that grafting human cells onto mice cells led to a 4 times increase in synaptic activity. Han and colleagues, as reported in this blog`s post of January 2015, supports this observation since they found that grafting of human glial progenitor cells in the mouse forebrain led to increased synaptic plasticity and learning in the adult linked to glial cell increases.  An increase in human astrocytes was observed at 4-5 months in the hippocampus and deep neocortex layers, but by 12-20 months it was also observed in other areas such as the amygdala, thalamus, neostriatum and cortex. An investigation into synaptic activity in neuronal cells from the hippocampal dentate granule layer, an area used because of its large number of engrafted cells and the region´s known role in spatial memory, found a significant increase in the engrafted human glia cell`s  basal level of excitatory synaptic transmission. This long term potentiation (LTP) enhancement was not linked to increased NMDA receptor activity (or increased glutamate release), altered adenosine concentrations or, changed D-serine release but instead to increased TNF alpha which induces the addition of AMPA receptors to neuronal membranes and AMPA GluR1. The insertion is regulated through protein kinase C (PKC)-mediated phosphorylation of appropriate sites. Such increased neuronal activity was mirrored by enhanced learning in the chimeric mice with increased spatial memory and quicker contextual fear and tone conditioning. This observation was supported by Hennessey and colleagues who showed that astrocytes in degenerating brains caused by acute sterile inflammatory insult are primed to produce exaggerated responses via strong nucleur localization of NK-kB subunit p65 and increased synthesis of the chemokines, CXCL1 and CCL2. Administration of IL-1beta and TNF-alpha produced a more robust response in degenerating rat brain than the control.

Another function of astrocytes is potassium spatial buffering which is where extracellular potassium ions occuring during stimulation are taken up. The potassium ions enter through potassium channels causing the astrocyte to depolarize. The specialized inwardly rectifying potassium channels involved in this are also known to be linked to GPC receptors and are ATP sensitive. Potassium ion entry increases the internal concentration which is dissipated over a large area by the extensive network of astrocyte processes. There is no evidence that these GPC receptors are linked to the calcium surges that induce higher sensitivity to neuronal firing, but if they are this could mean that the internal calcium ion surge may not be caused just by neurotransmitter binding on the astrocytic surface, but also by the inward current of potassium ions released by presynaptic cells on neuronal activation.

Therefore, astrocytes appear to be stimulated by the activation and release of neurotransmitters from the activated presynaptic neuronal cell, or possibly by the internalization of potassium ions excluded during firing. These actions affect G protein receptors activities and cause internal calcium surges which presumably activate protein kinases, phosphorylate relevant proteins and cause specific gene transcription changes just like in neuronal cells. It is possible that the result of this is the release of gliotransmitters (signal molecules that could be noradrenaline, glutamate or GABA) from the glial cells which will then bind to the relevant receptors on the post-synaptic membrane. The overall result of this is that there is possibly a ´second wave` stimulation of the post-synaptic membrane, the first being the direct binding of neurotransmitters released pre-synaptically directly on firing. This second wave is linked to AMPA receptor addition to the post-synaptic membrane which is associated with LTP. The implication of this ´second wave` response is the slight delay observed in post-synaptic effects. Therefore, the post-synaptic neuronal response is augmented and temporally extended by the astrocytic response to both neurotransmitter and potassium ion presence in the synaptic cleft. Using this hypothesis, we can suggest that if tDCS can cause astrocytic calcium ion surges, then it could work by ultimately causing effects that are normally associated with neurotransmitter firing triggered by other means. The excess of electrons administered by the direct current stimulation can cause changes in presynaptic membrane electron fields which can result in the release of neurotransmitters from the presynaptic neuronal cells and/or firing of the cell. The neurotransmitters released or the excluded potassium ions can then bind to the astrocytic G protein linked receptors and cause the effects described above. In this way, the ultimate result is AMPA receptor addition to the neurons and induced LTP. This changed plasticity will present as the changes in learning and depression suggested as being associated with tDCS.

Therefore, the role of astrocytes in the neuronal firing scenario and neuronal plasticity appears to be important and can possibly be manipulated by applying direct current. This may provide a mechanism by which neuronal areas understimulated or defective in normal stimulation can be induced to fire, but it may be detrimental in areas where hyperexcitability is being reported eg. in the hippocampus and entorhinal areas as recorded in dementia. Therefore, more research is needed to investigate this astrocytic effect.

Since we`re talking about the topic…………………………………..

………would using radioactively marked AMPA receptors in the hippocampal area of transgenic mice show that tDCS is linked to AMPA receptor insertion into the cell membrane?

……….can we assume that removal of astrocytic cellular calcium ions with EDTA will have an effect on neurotransmitter release?


Posted in astrocytes, calcium ions, glutamate receptors, tDCS, Uncategorized | Tagged , , ,