The neocortex is responsible for sensory perception, cognition, generation of motor commands, spatial reasoning and language. There are new theories being developed on how it works. One theory comes from a company named Numenta. Numenta was founded by an entrepreneur named Jeff Hawkins who invented the Palm Pilot (the first commercially successful example of a hand-held computing device), and then devoted himself to modeling the brain. To understand his theory you should read the book on Numenta’s website (Biological and Machine Intelligence). This blog post is examining a modification of Numenta’s ideas by another entrepreneur, Max Bennett, a cofounder of an AI company called “Bluecore”. The material is taken from his paper: “An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex”. I should say that a blog post allows for easier reading, because I leave out his citations and skip the neuroscience findings that argue for or against his model. One risk of doing that is that I confuse what is known with what is conjectured. Therefore, if the post interests you, you will find more ideas and more accurate information in the article).
Let’s start with the basics. As you can see from the picture, a pyramidal neuron has dendrites that come from its apex and also has basal dendrites that come in laterally. The former are called ‘apical’ dendrites and are thought to receive feedback from higher layers. The basal dendrites in this theory are thought to carry contextual information. The two concentric circles that span the image show the area near the cell body – the proximal area, and an area further away (the distal area). Most synapses on a neuron are in the distal region. A single synapse on the proximal area may be able to fire the neuron, but a synapse in the distal area would weakly depolarize the dendrite, and the signal would be lost by the time it would reach the cell body (also known as the Soma)
We could ask, if a synapse on a distal segment of a dendrite cannot fire a neuron, then what is it for? The answer is that if it fires in combination with other nearby synapses on the same dendrite segment it can create a dendritic NMDA spike. This spike is an action potential but is weaker than the action potential that would be generated at the Soma. The dendritic spike can travel to the soma and if strong enough, perhaps in combination with other NMDA spikes, can set off an axonal action potential that then travels along the axon of the neuron and releases neurotransmitter at outgoing synapses. Even if the neuron is not strongly stimulated enough to fire an axon action potential, a dendrite action potential causes enough of a depolarization to cause learning at the dendritic segment. This allows each dendritic segment to learn which inputs tend to go together, and so the segment becomes a pattern recognizer.
You can think of each segment as taking a logical AND of its inputs because if all the inputs of a pattern fire above a threshold, the segment emits a spike. (This is a simplification because the combination of the inputs is not linear.) The logical OR in the diagram below shows that any one of the dendritic segments can send a spike from the dendrites.
The neocortex is about the size of a napkin folded on the outside of the cerebrum. It has about 100 billion cells, 20% of which are inhibitory. It is made up of six layers. (Bennett is modeling the “sensory” neocortex, which has different functions than the “frontal” neocortex.)
To quote Bennett:
“Much of the connectivity of the neurons within a given area of the sensory cortex is horizontally contained within a 300 to 600 micron wide column, although spanning vertically across all six layers This “cortical macrocolumn” of local horizontal connectivity has been proposed to be the canonical neocortical microcircuit. The human neocortex is thought to be made up of over a million such macrocolumns. In order to decipher the computations within a macrocolumn, we must interpret the observed connectivity of each of these types of neurons.”
“L4 and L2/3 of a macrocolumn can also be subdivided vertically into about 80 to 100 minicolumns, each of which is about 50 microns wide. In our model macrocolumn there is one L4 stellate cell per minicolumn and many L2/3 cells within a minicolumn. Cells within L5 and L6 are not mapped to a specific minicolumn, but rather perform computations across the entire macrocolumn”
So the macrocolumn is divided into minicolumns, but the minicolumns do not span the entire macrocolumn, they just span layer L2/3 and L4. Bennett draws a diagram with different layers having different colors. As you can see, L2/3 and L4 are lined up in minicolumns.
So what are the layers for?
Layer 1 (L1) is closest to the skull, and Layers 5 and 6 are the deepest.
L1 is the main target of cortical and subcortical inputs that provide “top-down” information for context-dependent sensory processing. Although L1 is devoid of excitatory cells, it contains the distal dendrites of pyramidal cells (PCs) located in deeper layers.
L4 is where inputs from senses or from lower layers in a cortical hierarchy come in.
In the diagram below, layer 5 is divided into two areas, L5a and L5b. L5a has Regular Spiking neurons (L5a-RS) and L5b has Intrinsic Bursting neurons (L5b-IB).
Layer 6 is divided into two types of neurons, L6a-CT (Cortico-Thalamic), and L6-CC (Cortico-Cortical). The dendrites of the L6a-CT neurons actually extend to layer 4, which Max Bennett thinks implies that they get the same input as the L4 cells.
There are also inhibitory neurons in the layers, and multiple types of excitatory neurons.
A more detailed picture from Max Bennett’s paper follows:
Numenta’s “Hierarchical Temporal Memory” (HTM) models propose that pyramidal neurons always exist in one of three states: inactive, predictive, and active. In an inactive state, the neuron is highly polarized. In an active state, a neuron is firing action potentials. In a predictive state, a neuron is subthreshold depolarized. The computational purpose of this predictive state is that if a proximal synapse has a presynaptic action potential, neurons in predictive states will fire before neurons in inactive states. In parts of the neocortex with extensive lateral inhibition, this will lead to neurons that were in a predicted state firing action potentials, but those that were in inactive states not firing at all because they get rapidly inhibited before they have a chance to depolarize.
Suppose the two dark colored minicolumns shown on the left stand for the letter ‘B’. All the cells in the minicolumn, whether in L4 or in L2/3, are firing. Numenta’s idea was that you could have only a subset firing. This subset might have been predicted in a prior step, using lateral connections from other minicolumns. So a sequence “A” followed by “B” might have the representation for “A” sending out lateral connections that in the next step, partially depolarize other neurons, some of which would be in the representation for “B”. The depolarization would not be enough to fire a neuron, but would predispose it to firing quickly, and suppressing the neurons in the column that do not have excitatory connections from the representation of “A”. The picture on the right shows a sparser representation of “B” which signifies that “A” happened prior.
You can create many sparse combinations for “B” preceded by some other letter(s) if you think of “B” as being one or more excited neurons in the two minicolumns in this example so that at least one neuron in each column fires. You could have A->B, D->B, H->Q->B, for example, all representable by the two columns.
Max Bennett’s model diverges from Numenta here because he doesn’t think the lateral connections from L2/3 neurons to other L2/3 neurons are responsible for predicting the next neurons that will fire, instead he thinks that L2/L3 neurons synapse on L5a-RS neurons, which in turn synapse back on L2/L3 neurons, and so predictions are made by this indirect route.
I should say here that Numenta’s model so far doesn’t explain many of the higher functions of the neocortex. But they do show how sequences can be learned and recognized, and how objects can be recognized.
Bennett says this:
“…prior HTM models have not incorporated working memory. Second, it appears evident that different macrocolumns coordinate processing together at precise timescales, otherwise it would be impossible for macrocolumns organized in a hierarchy to integrate information accurately. However, I am unaware of a neural circuit model that explains how such precisely timed coordination occurs. Third, prior HTM models do not explicitly incorporate attention. As such, I seek to present a model that can perform the same computations of prior HTM models, but can also:
(1) perform working memory and connect sequences separated by long time intervals.
(2) coordinate its activity and processing with other macrocolumns and structures on extremely precise time intervals.
(3) can be modulated by attention.”
Much of the communication between macrocolumns in the sensory neocortex is done via the thalamus. Located deep in the brain, the thalamus is classically known for its roles as a sensory relay in visual, auditory, and somatosensory systems. It also has significant roles in motor activity, emotion, memory, arousal, and other sensorimotor association functions. The thalamus plays a big role in Bennett’s model.
From his article:
“The thalamus is primarily made up of excitatory thalamocortical relay neurons. Recent experimental studies have shown that there are three categories of these thalamocortical relay neurons within sensory thalamus: ‘‘Core Neurons,’’ ‘‘Multiareal Matrix Neurons,’’ and ‘‘Local Matrix Neurons’’. Each of these has different connectivity with the neocortex. Core neurons project directly to L4-ST neurons. Local Matrix Neurons project to layer 1 within a single level of the cortical hierarchy. Multiareal matrix neurons project to L5a, L1, and L3 across different levels of the hierarchy. Multiareal matrix neurons are also the only one of the three types of relay neurons that project directly to the striatum and the amygdala.”
“The thalamus is organized hierarchically, with ‘‘first order relay nuclei’’ passing information directly from peripheral senses (e.g., sight, sound, touch) to ‘‘first order neocortex,’’ while higher order relay nuclei pass information between different levels of neocortex within the hierarchy. Early on in this hierarchy, thalamic nuclei are separated by modalities, with separate nuclei for vision, audition, and somato-sensation.”
Then he explains how the macrocolumns connect to the thalamus. Recall that inputs to a macrocolumn feed in in the L4 layer, and they go there via “core relay neurons” in the thalamus. It seems that L6a-CT neurons project back down to those core relay neurons. L5b-IB neurons project from the macrocolumn to a higher level macrocolumn, and they do this via other core neurons that project to L4 in the higher level macrocolumn. I’ll show a diagram, but first lets read Bennett’s description:
“The nature of the connectivity between the thalamus and macrocolumns provides clues as to the computations that are being performed in thalamocortical networks. L5b-IB neurons provide driving input (synapses close to soma) to core relay neurons that project to L4 in other ‘‘higher-order’’ macrocolumns These higher-level macrocolumns seem to repeat the same pattern of relaying their L5b-IB output through even higher-level thalamic relays to even higher-level macrocolumns.
There is also evidence to suggest that L5b-IB neurons provide driving input to local matrix neurons, which project back to L1 in the originating macrocolumn. In contrast to L5b-IB neurons, L6a-CT neurons provide modulatory input (synapses far away from the soma) back to the relay neurons that projected to L4-ST neurons in a given macrocolumn. These L6a-CT projections are generally thought of as the origin of ‘‘top-down’’ signals. They are not able to drive action potentials in thalamic relay neurons on their own, but they can increase the firing rate of an already activated thalamic relay neuron via these modulatory synapses or put them into a subthreshold predictive state.
Surrounding the thalamus is a thin sheet of inhibitory neurons called the thalamic reticular nucleus (‘‘TRN’’). There are two classes of inhibitory neurons within TRN: PV neurons and SOM neurons. PV neurons inhibit core relay neurons while SOM neurons inhibit matrix neurons. PV neurons receive input from L6a-CT neurons in the neocortex, while SOM neurons do not receive any input from the neocortex.
“Layer 4 stellate (‘‘L4-ST’’) neurons are the receiver of bottom-up input from lower-order cortical areas, primarily passing information up from the thalamus). Similar to previous models, I propose that L4-ST neurons perform coincidence detection on this bottom-up input. L4-ST neurons provide strong driving input to all L2/3 cells within its minicolumn. This means that whenever a specific coincidence of input is detected in L4-ST neurons, an entire L2/3 minicolumn will be activated. Experimental evidence for this simple form of coincidence detection in L4-ST cells can be seen directly in their response properties. Input to L4-ST cells in V1 comes from first-order visual thalamus (LGN), which respond to on-center off-surround circular stimuli in specific locations in their receptive field. However, L4-ST neurons in V1 primarily respond to bars of light of specific orientations. This is exactly what would be expected if L4-ST neurons performed coincidence detection on their bottom-up input. A bar of light in a specific orientation is simply a coincidence of a specific set of on-center, off-surround circles.”
“ The pyramidal neurons found in L2/3 (‘‘L2/3-PY’’ neurons) have basal dendrites that extend laterally throughout the entire macrocolumn. They have apical dendrites that extend throughout L1 in the macrocolumn…..I propose the computation of individual L2/3-PY neurons is as described by the ‘‘HTM model neuron’’: basal dendrites receive ‘‘contextual’’ modulatory input from other L2/3-PY neurons, whereas apical dendrites receive ‘‘top-down’’ modulatory input from other macrocolumns and higher-order thalamus. Excitation of either apical or basal dendrites of L2/3-PY neurons does not provide sufficient depolarization to drive somatic depolarization. However, such subthreshold excitation can modulate the sensitivity of these neurons to L4-ST input.
“When other L2/3-PY neurons synapse directly onto L2/3- PY neuron dendrites, they provide excitatory contextual modulatory input. When they instead synapse first onto inhibitory interneurons, they provide inhibitory contextual modulatory input. I propose that this excitatory and inhibitory recurrent connectivity enables the L2/3-PY cell network to operate as a winner-take-all competitive network.
“Suppose a macrocolumn has learned two coincident patterns in L4-ST neurons—one pattern for ‘‘A’’ and one pattern for ‘‘B’’. This model proposes that the L4-ST neurons that respond to ‘‘A’’ will activate a set of minicolumns in L2/3, whereas the different pattern of L4-ST neurons that respond to ‘‘B,’’ will activate a different set of minicolumns in L2/3. I propose that the cells in a minicolumn active within ‘‘A’’ will provide excitatory input to neurons in other minicolumns also active in ‘‘A’’ while providing inhibitory input to neurons in minicolumns that are not active during ‘‘A’’ (such as those for ‘‘B’’). This effectively implements a competitive network, where cells responsive to ‘‘A’’ will excite other cells responsive to ‘‘A’’ while inhibiting those responsive to other stimuli.”
So to recap what Bennett is saying here, if a representation of “A” in L2/L3 consists of more than one column, then those columns excite each other. They also inhibit columns that are part of other representations.
Bennett also thinks this gives a mechanism for resolving inputs that could be interpreted multiple ways:
“This means that if ambiguous or conflicting coincidence detection occurs (i.e., both ‘‘A’’ and ‘‘B’’ are input into the network simultaneously), the competitive network in L2/3 will force only one representation to be active . Furthermore, top-down excitation enables higher cortical regions to bias L2/3 representation, allowing for patterns with less bottom-up input to still win. Note that top-down bias cannot create a representation if there is no bottom-up evidence at all, it can only bias representations. This is consistent with intuition—consider the famous duck or rabbit example. This image can be seen as either a duck or a rabbit, but you can’t see a unicorn. Top-down bias can shift network states between representations that have some bottom-up evidence but not to representations with no bottom-up evidence.”
The next figure shows this disambiguation idea. Part A (top) shows a representation for the letter “A” that consists of two columns. Part A bottom shows a representation of the letter “B” that also consists of two columns, but different columns than in “A”. Part B top shows that the two columns of the letter “A” excite each other. Part B bottom shows that the two columns of the letter “A” also inhibit the two columns of the letter “B”. Part “C” shows blue filled in circles in layer L4, with the dots for the letter “A” being a darker color – which is to indicate stronger evidence coming from the senses or from a lower level – than the evidence shown by the dots for the letter “B”. “A” outcompetes “B”, and suppresses “B” in this case, unless, as shown in Part “D”, top down information strengthens the L2/L3 representation of “B” enough so that it wins the competition. If the letter “B” wins, it suppresses the L2/L3 columns for the letter “A”. L4 is not affected by this top down disambiguation, but L2/L3 is.
Bennett notes that a single L2/L3 pyramidal neuron’s axon branches widely in L5, providing input to many L5a-RS neurons throughout a macrocolumn. L5a-RS neurons send a massive projection back to L2/3 neurons, synapsing both on pyramidal neurons and inhibitory interneurons throughout the macrocolumn
Suppose you input a rapid sequence of already known patterns (e.g., A, B, and C) into a macrocolumn; and suppose each element follows each other immediately with no delay. The figure below shows a series of proposed steps to learn the sequence “A,B,C”. In the figure, two column represent each letter, but that is and arbitrary number just for explanatory purposes.
The letter “A” is input on L4 (the blue dark dots) and this creates a representation for “A” in L2/L3 (the green dots). The pattern in L2/L3 projects down to the L5a-RS neurons, and activates some of them. The assumption is that there is a unique pattern of L5a-RS neurons that fires for every possible pattern in L2/L3.
The pattern of L5a-RS neurons sends axons to L2/L3, biasing (but not firing) an arbitrary pattern of neurons in L2/L3.
An input of the letter “B” comes in via L4. This activates the pattern for the letter “B” in L2/L3. Due to the prior biasing some of the neurons in the two columns shown for “B” are inhibited, some others are biased to be excited, so the pattern in those two columns is unique for “B preceded by A”. (there is a light error in the figure, the circles that just have outlines in L5a-RS should have been filled in).
The unique pattern for “B preceded by A” activates a pattern in L5a-RS.
The pattern in L5a-RS biases various neurons L2/L3. Then Input “C” comes in via L4, which in turn fires two columns in L2/L3. Some of the neurons in those two columns are biased to fire, other are biased to be inhibited. So the pattern in these two columns represent “C preceded by A,B”
In the figure Step 3 and Step 5 also have a note (underneath) that says that Hebbian plasticity (in this case spike timing dependent plasticity or STDP) occurs between the L5a-RS neurons and the sparse code that they biased. In other words, when “B” or “C” comes in, the L5a-RS neurons that biased the two columns also learn to fire those two columns in the same pattern that was created by the biases. This means that the sequence can be replayed and has been learned.
Bennett calls the process of biasing by L5a-RS neurons of L2/L3 neurons “sequence biasing”. Biasing is subthreshold activation and here the pattern produced by biasing (in this case in the two columns representing a sequence) is reflective of what came before in the sequence.
A problem for the above network to learn the sequence ‘‘ABC,’’ is that the patterns must be input rapidly within the < 100 ms time window for this short-term synaptic potentiation (the Hebbian plasticity) which is not realistic. Later in Bennet’s article, he comes up with a mechanism that would make longer time scales work – for instance “A” followed by a 5 second pause followed by “B” followed by a 5 second pause followed by “C”. His approach has a limitation that a sequence “A->B->C” would not be distinguishable from “A->B->B->C” unless some other factor is in play.
We talked about L5a-RS neurons. What do L5b-IB neurons do?
Bennett proposes that L5b-IB neurons perform pattern separation on the L2/3 macrocolumn code, meaning that the L5b-IB code is sensitive to the sequence representation in L2/3, not just the column representation. He proposes that this ‘‘unique sequence code’’ is the core output code of a macrocolumn. Its true that L5a-RS has a pattern that is also unique to a letter in a sequence, because as the sequence unfolds, it see saws between L2/L3 and L5a-RS, with sparse unique representations in L2/L3 leading to unique representations in L5a-RS. However L5b-IB can learn to represent the entire learned sequence, even as it just starts to be input. For instance, when “A’ is input, L5b-IB will output a code that stands for the learned sequence “A->B->C”. In the model, if “A” is also the start of another learned sequence, say “A->Q->W”, then that sequence can also be output at the same time from L5b-IB. This is one advantage of sparse patterns, because sparsity allows several patterns to be expressed at once without the patterns sharing neurons and interfering with each other. Here is a diagram that shows L2/L3 sending signals that create a unique pattern in L5b-IB, which in turn passes it to higher level macrocolumns via the thalamus. L5b-IB neurons also sends the pattern to other areas.
Here is a diagram from Bennett that shows L5b-IB patterns for different sequences before learning:
After learning, the sequence code output is for the entire sequence A->B->C, not just the single-element sequence “A” or the two-element sequence “A->B”:
Many models of the neocortex see it as a predictor of what comes next. Bennett proposes that layer 6a corticothalamic (‘‘L6a-CT’’) neurons encode predictions of the upcoming stimuli a macrocolumn expects. The observed connectivity of L6a-CT neurons is consistent with this. L6a-CT neurons have apical dendrites in L4 (red in the diagram below), where they have access to direct input from core thalamic neurons. Dendritic NMDA spikes in these apical dendrites can learn the same patterns that L4-ST dendrites do. In the diagram, L5b-IB neurons also synapse on L6a-CT neurons. L6a-CT neurons combine this information with the information from L4 (which is the input from the senses or from lower macrocolumns in the cortex hierarchy) and use it to send predictions back to L4. Those predictions are modulatory – they bias neurons, but they do not fire neurons.
Bennet thinks that L6a-CT neurons, just like L2/L3 neurons, are connected in a ‘winner take all’ fashion. They can be put in a predictive state (in this case by their apical dendrites coming from L4), and then the driving input from L5b-IB is sets off columns in L6a-CT that become sparse and unique because of the information from L4. The two pieces of information are the sequence the macrocolumn is currently in (that comes from L5b-IB) and the input that just come into the macrocolumn (that comes from the apical dendrites that reach into L4). If the macrocolumn “believes” it is in sequence “X,Y,Z” and the dendrites from L4 convey that “Y” has just come in, then L6a-CT will predict “Z” and send modulatory signals to L4 to indicate that “Z” is expected.
Bennett puts it this way:
“Suppose a specific coincident pattern of input is received by a macrocolumn. This puts a specific pattern of L4-ST neurons into an active state, as well as putting a specific pattern of L6a-CT neurons into a predictive state. Furthermore, suppose a given L5b-IB sequence code sends driving input to a random subset of L6a-CT neurons. When the L5b-IB sequence code fires, only the predicted L6a-CT neurons receiving L5b-IB input will become active, the rest will be inactivated by lateral inhibition. This generates a sparse L6a-CT code that is unique to a specific element within a specific sequence”
The predictions bias the neurons, and eventually, via Hebbian plasticity, learn to fire them.
“The random L6a-CT pattern that gets activated by ‘‘B’’ in the sequence ‘‘ABC’’ will fire right before the core thalamic neurons and L4-ST neurons for ‘‘C,’’ hence building short-term Hebbian plasticity with both of these neurons. Hence if the sequence ‘‘ABC’’ is replayed a sufficient quantity of times, these sparse L6a-CT codes will build long-term plasticity with the core thalamic and L4-ST neurons that tend to follow them, hence reliably predicting the upcoming element in a learned sequence.
In this figure from Bennett’s article, L6a-CT’s sparse pattern (step 2) sends predictions to L4 (step 3).
There is another use for predictions from L6a-CT
“I propose that another function of the frontal projection to L6a-CT and L2/3-PY neurons in the sensory cortex is to enable top-down attention. I use the term ‘‘top-down attention’’ to refer to two abilities–the ability of a subject to toggle between different possible interpretations of ambiguous stimuli and the ability of a subject to search an environment for specific features or objects (e.g., ‘‘where’s waldo?’’).
So Bennett thinks that attention is fundamentally a process of biased competition in a winner-take-all network.
He also has an explanation for working memory, (working memory’s definition includes memory for sequences, or for lists)
“A recent experimental study showed that L6a-CT neurons provide strong driving input to L5a-RS neurons, eliciting action potentials directly. If macrocolumns work as proposed here, then the activated L5a-RS neurons will activate L2/3-PY neurons. This means that if the frontal cortex triggers a specific L6a-CT representation (bloggers note: in the sensory cortex), then simultaneously it will trigger a corresponding L2/3-PY representation via L5a-RS neurons. In other words, a frontal projection to L6aCT can trigger and maintain L2/3-PY representations without sensory input.”
The actual working memory would be a sequence playing out in a macrocolumn via the ‘see saw’ between L5a-RS and L2/L3.
Many sequences would play simultaneously in many macrocolumns, so coordinating them in time would be important.
For working memory to play out in L2/L3, L4 would have to temporarily be turned off. (Recall that L4 gets inputs from the outside world directly or indirectly and it would interfere with a replay of memory in this model because it synapses on L2/L3 neurons). Bennett explains how this might be accomplished but before I discuss that here, I should describe why the Hippocampus appears in the above diagram.
“I propose that the hippocampus is an essential component of this process. CA1 within the hippocampus has been shown to replay place codes on the gamma rhythm during working memory tasks “
(bloggers interruption: gamma waves are high frequency: 25-140 Hz while theta is relatively low frequency: 4–7 Hz and alpha is somewhat higher than theta 8–12 Hz. A place cell is a kind of pyramidal neuron within the hippocampus that becomes active when an animal enters a particular place in its environment, which is known as the place field. Place cells are thought, collectively, to act as a cognitive representation of a specific location in space.)
“CA1 of the hippocampus provides an extensive excitatory projection to the frontal cortex. If CA1 triggers replay in the frontal cortex, then the corresponding representations within the sensory cortex could also be replayed due to already described frontal projection to L6a-CT neurons.”
So one possibility here is that as a mammal moves through an environment, and the different place cells in the hippocampus fire, those place cells are telling each macrocolumn to remember an episode related to the various places it passes through.
One problem that has to be solved is coordination:
“Let us now turn to answer the question of how the brain coordinates processing across macrocolumns on precise timescales. Processing on precise time scales is an essential requirement for networks of macrocolumns. Postsynaptic excitation after presynaptic excitation across a single synapse, in the absence of successfully driving a postsynaptic spike, typically decays for 10–30 ms). This means that in order for dendritic segments to sum inputs across multiple synapses, presynaptic neurons must fire action potentials within a precise time window.
I propose processing on precise timescales is made possible by macrocolumns oscillating back and forth between an ‘‘input state’’ and an ‘‘output state.’’ The inherent circuit dynamics within the thalamus ensure that macrocolumns oscillate between these states at the same time, enabling coordinated processing. Within the thalamus, about 30% of thalamocortical cells have been called ‘‘High-Threshold Busting Cell’’ (HTC) due to their rhythmic bursting at the alpha rhythm. When these HTC neurons burst fire they inhibit other thalamic relay neurons via thalamic interneurons. I speculate that these HTC cells are in fact the same as the multiareal matrix cells and the neurons they inhibit are core relay neurons. If this is true, then on the alpha rhythm, multiareal matrix neurons will fire for ~50 ms while core neurons pause, and then core neurons will fire for 50 ms while multiareal matrix neurons pause, back and forth.
I propose that when multiareal neurons pause and core thalamic neurons are activated, macrocolumns lock into an ‘‘input state.’’ In this state, macrocolumns integrate bottom-up input from core thalamic neurons through L4-ST neurons and top-down input through apical dendrites of L2/3-PY neurons. Activation of L4-ST neurons excites inhibitory interneurons in L5 which directly inhibit L5a-RS and L5b-IB neurons. Hence during input states, superficial layers are activated, and deep layers are inactivated. “
The green arrows in the diagram are inhibitory, so you can see that L4 suppresses the L5 layers from sending outputs.
“However, when multiareal matrix neurons burst fire and core relay neurons pause, the macrocolumn shifts to an ‘‘output state.’’ In this state, I propose that L2/3-PY and L4-ST neurons will be inhibited, while deep layer neurons will become activated. There are several ways in which this could happen…”
The blogger’s diagram below shows one way: The HTC neurons, no longer inhibited by the core neurons, send a projection to L1, where they synapse on L5b-IB neurons, whose axons fire L6a-CT neurons, which in turn inhibit L4 and the L2/L3 layer.
Why have an output state?
“First, the output state enables a stable output of the L5b-IB sequence code, so that it can be passed to other regions without being interrupted by changes in sensory input.
Second, the output state enables the macrocolumn to reactivate memories within L2/3-PY via L6a-CT neurons without being disrupted by incoming sensory information through L4-ST.
Third, it provides a mechanism for macrocolumns to ‘‘reset’’ their representations in concert, and hence enable a network to re-lock into a new representation given new information.”
Given that in the output state, L2/3 is inactivated by inhibitory neurons from L6 you might ask how can it be used for working memories sparked by L6a-CT?
The answer is that this inhibition must be short lived, serving the purpose of wiping any remnant L2/3 representations while a L5a-RS representation is activated, so that by the time the L5a-RS code gets to L2/3, the neurons there are all in a passive state, ready to be activate a new representation (i.e. with no contextual lateral biasing from previous representations).
“I propose that there are two broad oscillatory modes of sensory thalamocortical networks: passive processing and attentive processing, each coordinating processing between different sets of regions at different frequencies.
I propose passive processing is the default thalamocortical network mode within the sensory cortex. In passive processing macrocolumns oscillate between input and output states at the alpha rhythm, spending roughly 50 ms in each state. …
However, I propose that during situations requiring top-down attention or working memory, thalamocortical networks slow down their oscillations to the theta frequency (about 100 ms in each state).”
I propose that the purpose of this oscillatory slowing is threefold.
First, the default oscillatory dynamics of the higher-order frontal cortex and hippocampus are in the theta frequency, hence to coordinate processing with those regions’ sensory cortex needs to also oscillate at the same rhythm.
Second, this slowing down gives L2/3-PY neurons more time in between input states to replay sequences, hence enabling more items to be stored in working memory. Third, this slowing gives L2/3-PY neurons more time to lock into a representation that well matches top-down input and bottom-up input.
…I propose that during periods of a good match between top-down expectations and bottoms up input, L2/3-PY neurons resonate at gamma oscillations…As proposed by others, I hypothesize that the function of these rapid oscillations during successful predictions facilitates long-term synaptic plasticity to learn new associations of objects and sequences being attended to.”
In the third item above Bennett says that if the top down information corroborates the bottom-up information there is more input to the L2/3 Pyramidal neurons, and their spiking oscillates at the gamma frequency which also allows for learning. (In another part of his article, that I won’t discuss here, Bennett explains how mismatches between expected and actual inputs creates a signal of “surprise”, and that too involves a matching (or lack of matching) between top-down signals with bottom-up signals, this time in the core neurons of the thalamus).
In the second item above, Bennett is saying that the slowdown in frequency allows more time for working memory. This explains a finding known since 1956:
“Psychologist George Miller showed that the average human can only hold around seven items in short-term working memory at a given point in time. However, a neural circuit explanation for why we have this working memory limitation has been elusive. Lisman and Idiart made the novel observation that the two frequencies observed in EEGs during working memory tasks, theta and gamma oscillations, have a clear relationship with the ‘‘magic number 7’’: there are ~7 gamma oscillations within one half of a theta wave (~100 ms). They went on to propose that elements in working memory are replayed at the gamma frequency every theta cycle.
Consistent with their idea, I propose that the reason we have this limitation is that the thalamocortical networks provide a maximum of ~100 ms within an output …”
We’ll end this blog post with Bennett’s model of how a macrocolumn can learn a sequence over realistic timescales. For instance, learning a sequence of letters: “A”, “B”, “C” where there is a pause of 5 seconds between letters.
From the article:
“ In our model macrocolumn, let us represent these different elements (‘‘A,’’ ‘‘B,’’ ‘‘C’’) by the activation of different sets of two minicolumns (see Figure 3A). Computationally five specific states will occur during the example procedure of learning this sequence:
(1) Receiving the input of ‘‘A’’ for 1 s:
(2) Pause (no input) for 5 s:
(3) Receiving the input of ‘‘B’’ for 1 s:
(4) Pause (no input) for 5 s:
(5) Receiving the input of ‘‘C’’ for 1 s.”
In step 1, figure 13 from the article shows a theta wave in the second row. The down cycle coincides with the input state of a macrocolumn, and the up cycle coincides with the output state. The third row shows several steps, but note the arrows from the theta wave diagram in the middle to the steps on the bottom – more steps occur on the up cycle, and so visually they don’t coincide on the diagram. Anyway, Bennett thinks that an ‘episode code’ from either the hippocampus or the frontal cortex, creates a random pattern in L6a-CT. L6a-CT feeds into L5a-RS, where it activates another random pattern. At this point though, “A” is input to L4 which creates a two-column representation for “A” in L2/L3. There is short term potentiation that occurs here, so the random pattern in L5a-RS gets associated with the pattern in L2/L3. L2/L3 then activates a sequence representation in L5b-IB. Then L2/L3 neurons all stop firing. Bennet explains this: “after being initially triggered from the prior activation of ‘‘A’’ in L2/3, the L5b-IB representation turns off any further L2/3 activation (by activating L6a-CT neurons, which then inhibit L4-ST neurons).
So we see a step labeled “Output code for single element sequence ‘A’”. This code is coming from L5b-IB (the orange colored row). Note that this code comes during the output state, which is in the up-cycle of the theta wave. Now the episode code, which is still present and coming from the frontal cortex or hippocampus, triggers the L5a-RS neurons again, creating the pattern it did before, which replays “A” in L2/L3. The L5b-IB neurons do not change, they are still firing the code for sequence “A”. The L5a-RS neurons bias the L2/L3 network in a characteristic way, so that if “A” continues to be input, it will create the same pattern in L2/L3.
Now what happens during the 5 second pause after “A”? Bennett explains:
“When the sensory input of ‘‘A’’ is removed during the pause, as long as the frontal/CA1 episode code continues to replay itself during this delay period, then the L6a-CT episode code will continue to independently replay ‘‘A’’ during each output state. Crucially, this means that at the beginning of each input state. L2/3 is sequence biased from the L5a-RS ‘‘A’’ representation, waiting to be mapped to the next incoming L2/3 representation. I propose that this continuous replay of an episode code is one of the key underlying computational processes performed by the brain during working memory tasks.”
I’ll use Bennet’s explanation here:
Step #3: Input “B” For 1 s
After 5 s of a pause, the sensory input of ‘‘B’’ is provided to the macrocolumn. Due to the sequence biasing from L5a-RS neurons, a sparse representation of ‘‘B’’ is activated that is unique to ‘‘A –> B,’’ and the L5a-RS code is mapped to this sparse representation of B using STDP. Due to this unique representation of ‘‘B,’’ now L5b-IB neurons output an ‘‘A –> B’’ sequence code instead of just the ‘‘A’’ sequence code
Taken together, this means that although ‘‘A’’ and ‘‘B’’ were separated by 5 s, in the macrocolumn they were only separated by –> 10 ms due to the repeated working memory replay of ‘‘A.’’ This enables rapid STDP plasticity between the L5a-RS neurons activated by ‘‘A’’ and the L2/3 representation of ‘‘B,’’ despite a 5-s separation between the actual sensory stimuli.
When the repeating frontal/CA1 episode code comes around and reactivates ‘‘A’’ during the output state, the entire sequence ‘‘A –> B’’ will be replayed automatically, instead of just ‘‘A.’’
Step #4: Pause For 5 s
Due to the same dynamics described in step #2, as long as frontal cortex/CA1 continues to replay the same episode code, our model macrocolumn will continue to replay the sequence ‘‘A –> B’’ on each output state even when stimuli ‘‘B’’ is removed.
The key difference between step #4 and step #2 is that now: (a) there are two elements replayed and hence two gamma cycles (A and then B); and (b) the output state now ends with a sequence bias from the L5a-RS code for ‘‘A –> B,’’ instead of the L5a-RS code for just ‘‘A.’’
Step #5: Input “C” For 1 s
When ‘‘C’’ is finally inputted into the macro column after the final 5-s interval, as in step #3, the sequence bias from L5a-RS code for ‘‘A –> B’’ leads to a sparse representation of ‘‘C’’ that corresponds to the sequence ‘‘A –> B –> C’’ (see Figure 15). This builds plasticity between the L5a-RS code for ‘‘A –> B’’ and this sparse representation of ‘‘C.’’ Hence now when ‘‘A’’ is replayed during the output state, there will be 3 elements replayed (hence 3 gamma cycles): ‘‘A’’ then ‘‘B’’ then ‘‘C.’’
During the output state, due to the L2/3 representation of ‘‘C’’ that is unique to ‘‘A –> B –> C,’’ the L5b-IB output code will now be a unique code that represents exactly the sequence ‘‘A –> B –> C’’ This macrocolumn has accomplished something amazing—it is now outputting a unique sequence code for the sequence ‘‘A –> B –> C’’ even though the input elements were separated by long time intervals. And the only external computation required was a constant episode code from the frontal cortex and/or hippocampus to enable consistent replay of only the first element ‘‘A.’’
Remembering the Sequence “ABC” After Just Saying “A”
Each time the sequence ‘‘A’’ then ‘‘B’’ then ‘‘C’’ replays during an output state while L5b-IB neurons are firing the ‘‘A –> B –> C’’ output code, each representation of ‘‘A’’ then ‘‘A –> B’’ and then ‘‘A –> B –> C’’ builds plasticity with L5b-IB representation of ‘‘A –> B –> C’’ (since they coactivate with each other). If this replay occurs a sufficient quantity of times, these synaptic connections will go through long-term potentiation (LTP). This LTP then makes it such that when this macrocolumn receives the input ‘‘A,’’ during the output state it will output the code ‘‘A –> B –> C’’ automatically instead of just the output for sequence ‘‘A.’’ Note that multiple L5b-IB representations can be active simultaneously, meaning that if ‘‘A’’ leads to multiple different sequences, multiple ambiguous sequence codes can be output for higher cortical areas to disambiguate.
One problem with the model is the following. The episode code activates L6a-CT neurons which put a random pattern into L5a-RS. Now an input of “A” comes in and the pattern in L5a-RS is associated with “A” in L2/L3. Suppose “A” in L5a-RS biases L2/3 as “A” is first learned so that “A” is a sparse pattern. Could it put some of the “A” neurons in L2/3 in inhibit mode, so that it modifies the original pattern of “A” in L2/3?
I emailed Max to ask him this, and he replied:
“The model would run into some problems if that occurred. For example, if I know 5 sequences starting with A, and an episode code creates a sparse representation of A, then the macrocolumn won’t be able to correctly predict what next elements might occur.
Admittedly however, I don’t specify a mechanism by which the first object attended to is not L5a-RS biased, while others are. But this is likely required for the model to work.
However, if episode codes get entrained to sensory cortex only in specific points in time (such as when I actively am attending to something), then this loss in predicting what will happen next may not be as severe. An interesting implication of this would be – if you are attending to something (hence entraining episode codes to sensory cortex), you could actually understand something less (since this biasing of a known object).”
Max’s theory unites a lot of unexplained phenomena like what oscillations in the brain might be for, how they work together, and how episodic memories might be stored. Episodic memories are memories of episodes that happened to you, as opposed to ‘semantic memory’ which is memory of facts. There are exciting implications, such that a place cells firing in a sequence in time could generate a memory of a traversal of a space with landmarks or even a more abstract ‘train of thought’.
Max also created a diagram of the neocortex with the known connections between the different types of neurons which is worth looking at for anyone who wishes to understand of model it. Again, the article is:
An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex by Max Bennett – Frontiers in Neural Circuits