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Thalamocortical architecture

by Gideon Kowadlo and David Rawlinson


One of the keys to understanding the neocortex as a whole, and the emergence of intelligence, is to understand how the cortical hierarchical levels interconnect. This includes:

  • the physical connections,
  • the meaning of the signals being transmitted,
  • and possibly also the way the signal is encoded.
Physical connections: Physical connections refer to gross patterns of neuron routing throughout the brain. This is known as the connectome. Below is an image from the Human Connectome Project, that beautifully illustrates many connections including thalamocortical ones.
Figure 1. Courtesy of the Laboratory of Neuro Imaging and Martinos Center for Biomedical Imaging, Consortium of the Human Connectome Project –

Meaning of signals: One classification that can be applied to thalamocortical neurons is drivers versus modulators. A driver can be thought of as a neuron that carries information, whereas a modulator modulates or alters the transmission of information in a driver. They have different functional and anatomical properties, as nicely described in (Sherman and Guillery 2011). If a neuron is a driver, what information does it encode, and if it is a modulator, is it inhibiting or excitatory and what effect does this have?

Signal Encoding: Signal encoding refers to the details of how the information is represented. This includes timing and amplitude information. The way the signal is encoded in the neurons may have a bearing on the properties of the system. Specific information has been added to the diagram where this looks relevant.

Our aim is to build AI with general intelligence characteristic of biological organisms such as primates. Therefore, we draw inspiration and insight from these working examples. Understanding the biology obviously gives us the best insight into how to do that. However, what level of abstraction do we need to capture the essential qualities?

  • at the lowest level: molecular structure, interactions and neurotransmitters,
  • above that, firing patterns and newly discovered molecular machinery (that excitingly shows this is more complex and interesting than previously thought – see paper and work by Seth Grant),
  • higher still, the brain as a set of modules that interact with each other,
  • or multi scale simulation of the whole brain (see the Human Brain Project).

For simplicity, we want to understand it at the highest level that is still capable of capturing the essential qualities, and drill down where necessary. Therefore, are factors such as the way that the signal is encoded important? Not in and of themselves, but they may have a bearing on emergent qualities, that are significant.

In order to understand the above, including drawing conclusions about the appropriate level of abstraction, we’ve elaborated on a figure first published in the CLA White Paper that was included in a previous post (in the section ‘Regions’). In that article, we started to explore these topics in the context of Numenta‘s work. The figure shows the thalamo-cortical connections to specific cortical layers and is very useful for exploring the concepts described above. Here, we will expand on that figure, shown below. We will go over a first version, and we plan to make further posts in the future, as we develop it further. Each of the initial annotations are explained in the sub sections below.

Figure 2. Thalamocortical architecture including cortical layers and connections between hierarchy levels. This figure is an annotated version of a figure from the ‘CLA White Paper‘. Some information is added from the text of that document. Other sources used are Sherman and Guillery 2011Grossberg 2007, Sherman 2006 and Sherman 2007.

We invite the community to make use of and contribute to this annotated diagram. The diagram is publicly available in a universal vector graphics format called SVG. Being vector based, it is easily modifiable. SVG is a common format, which many graphics packages are capable of editing.

The file is available from a git repository hosted on github called cortico-thalamic-circuit. Anyone can download, clone, make a pull request or fork the repository.

Pull requests allow you to make modifications and then give them back to the shared repository so that they are available to everyone. This is the action to take if you share our purpose for the diagram – staying as high level as possible, filling in details where they contribute to a holistic view or emergent properties of the thalamocortical architecture. Forking allows you to create a new repository that diverges from the main one. Use this option if you’d like to use the diagram for a different purpose, such as documentation of all the neurotransmitters in the different pathways.

The first set of diagram additions are described below.

Diagram Additions

Cortico-Cortical Feedback

The illustrated feedback between levels from layer 6 in Level (n+1) to layer 1 in Level (n) is described briefly in the CLA white paper. We have included an additional illustration from Grossberg 2007 (see figure 3 below), that shows in more detail how internal neural circuitry completes the intra-cortical, inter-level, feedback loop from:

H[n+1]:C[6] → H[n]:d[1]C[5]
H[n]:d[1]C[5] → H[n]:C[6]
H[n]:C[6] → H[n]:C[4]

Note: The connections above are described in a notation we have adopted for succinctly describing cortical neural pathways. Refer to our post for more details.

Figure 3. Inter-level feedback loop, reproduced from Grossberg 2007. The circles and triangles are neuron bodies, with varying shape depicting different neuron types. Two hierarchy levels are shown (V1,V2 from the visual cortex). Each hierarchy level has 6 cortical layers (numbered 1 to 6 where relevant). You can see that feedback from V2 affects activation of neurons in V1 layer 4.
The feedforward/feedback architecture gives rise to at least three important qualities, the first of which has been explored in the MPF literature. They are described below, reproduced from Grossberg 2007:

  1. the developmental and learning processes whereby the cortex shapes its circuits to match environmental constraints in a stable way through time; 
  2. the binding process whereby cortex groups distributed data into coherent object representations that remain sensitive to analog properties of the environment; and 
  3. the attentional process whereby cortex selectively processes important events. 

We may elaborate with a summary of Grossberg 2007 in a future post.

Gating by the Thalamus

Our main references for this section are Sherman 2006 and Sherman 2007.

We’ve seen that the thalamus acts as a relay for information passing up the hierarchy between cortical levels, which we’re referring to as the feedforward indirect pathway (FF Indirect). It has been postulated that via this gating, the thalamus plays an important role in attention.

What inputs and computations determine that gating? This is one of the questions we are attempting to learn more about, and so have explored inputs to the gating.

Cortical feedback

One of the significant inputs is FB from Layer 6 in the level above. That is to say that the gating from Level (n) to (n+1), is modulated by FB from Layer 6 in Level (n+1).

Thalamic feedback and TRN

There is a substructure of the Thalamus called the Thalamic Reticular Nucleus (TRN) that receives cortical and thalamic excitatory input, and sends inhibitory inputs to the relay cells of the thalamus.

These gating cells also receive inhibitory input from other Thalamic cells, labelled interneurons. Thalamic interneurons receive input from the very same relay cells, layer 6 of the cortex and the brainstem.

These circuits between TRN, BRF and thalamus are complex. They are simplified in the figure below, which appears in Sherman 2006 (Scholarpedia on the Thalamus), a version of which is found in Sherman 2007.

Figure 4. “Schematic diagram of circuitry for the lateral geniculate nucleus. The inputs to relay cells are shown along with the relevant neurotransmitters and postsynaptic receptors (ionotropic and metabotropic) Abbreviations: LGN, lateral geniculate nucleus; BRF, brainstem reticular formation; TRN, thalamic reticular nucleus.” Caption and figure reproduced from Sherman 2006.
We are currently representing this complexity as a black box (as shown in the diagram) that receives input from the Thalamus, BRF and cortex, and inhibits the relay cells. The purpose and transfer function require analysis and exploration. It may be necessary to model the complexity explained above, or some simpler equivalent may provide the necessary functionality.


The BRF is the Brainstem Reticular Formation, which as the name suggests, is a part of the brainstem. It has a number of functions that could be very important for attention and general functioning of the cortex, and therefore, we have included it and it’s connections to the Thalamus. Some of these functions include:

  1. Somatic motor control
  2. Cardiovascular control
  3. Pain modulation
  4. Sleep and consciousness
  5. Habituation

The Wikipedia page for the BRF gives a very good summary.

Modulation Signal Characteristics

It is interesting to note that the firing mechanism for the BRF and Layer 6 modulation of the Thalamic relay is Burst Mode rather than the more common Tonic Mode. Tonic firing has a frequency that is proportional to the ‘activation’ of a neuron. The frequency can be interpreted as the “strength” of the signal. Some have interpreted it in the past as a probability or confidence value. For Burst Mode firing, after a ‘silent’ period, the initial firing pattern is a burst of activity. This “results in a very different message relayed to cortex, depending on the recent voltage history of the relay cell” (Sherman 2006). It is thought that this acts as a ‘wake up call’ to the cortex when there has been some external change. We plan to speculate and elaborate further on possible purposes of this in the future.

Timing Information

The CLA White Paper makes mention of timing information being fed back from the thalamus to layer 5 via layer 1. This has been added to the diagram for visibility. It is thought to be important for prediction of the next state at the appropriate time.

Other Factors

There are a number of other significant brain components that may substantially affect the operation of the neocortex. Based on the literature, the most significant of these is probably the Basal Ganglia, which forms circuits with the Thalamus and Cortex. Another interesting and possibly important component are Betz cells, which directly drive muscles from the cortex.


This post was a first attempt to create an enhanced diagram of cortical layers and thalamocortical connectivity in the context of MPF/HTM/CLA theory. We’ll continue to elaborate on this in future posts.

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