Grids into Maps!

I answered directly in the other thread:

I haven’t put the emphasis on inter and intra cortical connections in my response, so it is not very related to my drawing presented above.

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This feedforward is up the hierarchy through the thalamus, right? Can you explain the feedback connection in more detail?

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These feedforward and feedback connections are corticocortical connections. No thalamus involved.

The following illustration shows corticocortical connections between areas of similar cytoarchitecture (we can say similar hierarchical level to simplify):

If the represented cortical areas have different hierarchical level, the connections are not symmetrical :

  • Corticocortical connections from granular to agranular tend to stop earlier in deep layers (commonly referred to as feedforwards, but this term is sometimes confusing because we like to think of the prefrontal cortex as the higher level but in fact, the main direction flows are going towards the motor cortex, see next illustration).
  • Corticocortical connections from agranular to granular tend to finish more in L1 (commonly referred to as feedbacks)

Those different connection patterns come from temporal differences in cortical development between agranular (early) and granular (late) areas


The feedforward pathway through the thalamus is an additional pathway completely different from this one (I am currently working on this slide).

You can have a look at this good illustration from Sherman (red arrows are the “ground truth” signal in the 3VS paper, but the predictions from L6 CT are not represented here)



Here it is:


Thanks, I guess CT cells are inhibitory?

All cells in the previous diagram are excitatory (including CT cells).

They are locally surrounded by inhibitory interneurons and it is not yet clear if excitatory cells project directly to other excitatory cells or to inhibitory interneurons. Both cases probably exist.


Would it be accurate for me to say that by outputting the exact opposite of any input signal received as in my vesicle membrane project “I have been experimenting with the fundamental wave generating behavior of reciprocal excitatory connections found in intra-area coupling”?

I can’t help but see what looks to me like the exact same thing drawn on the surface of your illustration:

Gary, if I understand this correctly the wave action is coming from the thalamus to act as a coordinating control function with the cortical connections as the data that is being synchronized.

I am not absolutely certain on this as I have not spent enough time studying the thalamus but it looks to me that your work on waves would match up most closely with some inner working of the thalamus.

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I’m trying to picture how that would work. Unfortunately the inner workings of the thalamus is still for me a complicated problem.

In the illustration the connections I’m looking at would only be the blue ones on the very surface. Everything else connected to it from below would change the behavior at that location, which in turn past that point changes the pattern of the traveling wave.

Although it’s hard to say whether it’s used as such: at the far end of each area a 2D map would become 1D signals over time, sort of a unique address that depends on what was mapped onto the 2D area. How the whiskers of an animal were brushed would show up in the complex pattern that the barrel cortex cells end up propagating outward to others, a way for each in the network area to sense unique experiences that happen in the external 3D environment.

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A thought experiment for you to consider - how does the completely intermixed learned patters embedded in the cortex all play out in the same wave pattern during experience and recall? What distinguishes them one from another?
In the scheme I am proposing - the wave interrogates the contents and coordinates the sender and receiver between maps with no regard to the contents so the wave shape can be the same for both.

I can’t see how the contents form the wave; unless you can offer some explanation to tie the two mechanisms tougher I can’s see how it would work.

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In this case I’m thinking more like what does a single cell that already has a good ability to predict and respond to events get out of helping to propagate 2D (stadium) waves with a pattern resembling what is being sensed happening in the external environment.

I also see it as a “wave interrogates the contents”. In the ID Lab-6 the contents would be walls bashed into and prior memories of shock zone locations at that time, which stop propagating the waves being started by the location with food in it. The waves that bounce off or adsorbed by the mind theater’s wall and avoid locations produces a vector map showing all the safe places to travel, paths towards safety.

Which cells are you referring to? E.g. L5 CT and interneurons surrounding them, TRN, interneurons in other thalamic nuclei, etc.

You see that this is where the fact’s don’t support the concept. The videos that I have seen show the waves sweeping over the cortex without any diversions.

Thinking about this a little more deeply - the contents of individual maps/areas is a distributed representation. It is fair to say that the representation is really distributed up and down the entire hierarchy but I will restrict that to a single map/area for this post. There is not a local “wall” to bounce off of. The wave has to sweep across the entire map/area to do whatever processing that happens in that area of cortex. I expect that the idea of a wall is not fully formed until the wave complete a pass across the entire map/area.

I also see the processing of things like paths and goals as being far more distributed in time than a single map/area. I strongly suspect that going from perception to action involves the entire hierarchy up to the temporal lobe, a pass through the subcortical structures out to the forebrain to be elaborated into action.
The various maps/areas have to decompose the sensations into feature clouds for recognition. This model does not do things like goal selection and object avoidance in single maps/areas.

I can easily see how a very much simpler life form could do the kind of processing like the slime mold offered for example but by the time you get up to worms and insects most of the brains have evolved far past this simple model.

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That can be true for some areas. What I see in the network are often spirals and complex waves similar to these:

Totally stopping the a traveling wave would require a large number of places leaving no holes to get through, otherwise the wave goes around all obstacles. Also from what I read a barrier/boundary cell becomes active when near one, is not always active.

This would be what the navigational part of the distributed system sums up to, where there is one or many blobby representations of movement, instead of detailed picture somewhere on the cortex. To record one one the animal would have to at least in its mind be navigating a complex environment with tunnels or other features that stand out in the wave pattern.

Concerning the excitatory cells, I was referring to L5 PT and L6 CT cortical pyramidal cells that project to the thalamus, and to the miscalled thalamic “relay” cells (“miscalled” because they probably do more than a simple relay)

It seems that virtually all axons between the thalamus and the cortex give some collaterals in the TRN (which sends inhibitory inputs to thalamic relay cells and/or other inhibitory thalamic interneurons).

This illustration from Sherman’s video shows the different options:

Other illustrations from the same video:

I hope it helps. On my side, I don’t know much about the internal thalamic computations.


I see I am doing a poor job of communicating the contents of maps/areas. You are describing things as objects in some map/area as if they were present as discrete objects distributed over the area like an overhead picture of the scene in a photograph. It just does not work that way at all.

The eye is an approachable path as we can form some mental models of how what we see is represented in cortex. The other senses work the same way but we will stick with the eye here. Even a quick overview shows that the idea of a wall in the cortex simply does not make sense. What we “look at” to form the concept of a wall is a sequence of fixations on the features of the world. I offer this bit about “just” looking at a face:

Note that the cortex is getting the images from each fixation as a sequences, each stacked one on the next. The fixations that is part of a wall or door a occupy the exact same space in cortex. What would a wave bounce off of?

It’s harder than that as the dynamics of a traveling wave are not synchronized to what parts the eye is pointing at - the door or the table in the way or … whatever.

The assemblage of features from different fixations are assembled into objects in the temporal lobe but these cortical waves that drive processing happen over the entire cortex - not just the bits where features turn into object and relationships between objects.


@Gary_Gaulin: I feel lost when trying to understand your experiments, even after reading your discussion with @Bitking

Could you state again what you would like to demonstrate concerning the travelling waves? What parts are facts, what are speculations, and how your experiments support (or will support) your speculations?

Can we reliably associate travelling waves (that we can measure with EEG) with L2/3 activity? I guess so, but no so sure.


The opening post of this thread to explain what I can has the paper “Dynamic Grouping of Hippocampal Neural Activity During Cognitive Control of Two Spatial Frames” I modeled from:

In the video I made you can see “Two Spatial Frames” working together, in each picture showing the network before and after propagating signals at that timestep in time. One frame maps the relevant memories of moving objects into the picture and the other frame maps the stationary objects, which are in this case (other than the food) invisible. It’s not necessary to map out the whole room and all in it at the same time, but in code it’s easiest to include all anyway.

It’s maybe still too early to know how well it models biology, but the average signal ratio matches live animal recordings and the virtual critter does as well or better than a live rat, at a very difficult task. I had to speculate in regards to the network rules that here turned out to only require those of reciprocal connections, to produce a vector map to navigate from.


Thanks for the clarification. At least in the rodent somatosensory system, L5 doesn’t form collaterals in the TRN, or so rarely that they’re probably just an artifact of biological processes. All other types of connections between cortex and thalamus form collaterals in the TRN. That includes both upper and lower L6 CT cells, which have different but not mutually exclusive targets in thalamus and TRN.

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I was thinking that this quick slide presentation for the “scientific method” might help explain the (HTM-like with ~28 dense SDR bits limit) motor memory system where HTM should work too. Success or failure at a prediction would depend on whether the guessed response motor action brings the head/body direction closer or further away from the (travels upstream) wave direction at that place in the “map”.

For quick responses it’s best to not go too far with resolution. Blobby areas of motion still get us around.

You would know you have the HTM part right by it reversing direction after feeling the confidence destroying zap, instead of like a zombie stop for nothing regardless of how bad and take the shock. There is then a protective part of itself learning how to avoid trouble.

Where shock is made less unpleasant by no longer sensing that bit at every timestep the critter will at some point intentionally endure the discomfort, depending how often the hungry bit chimes in. In the video it’s hungry all the time, but where not it could like go take a nap in the shock free center, then when hungry enough be back in action.

Examples like becoming noticeably agitated when the shock zone is around the food help demonstrate the complex behaviors expected of a real animal, without having to code anything other than a motor HTM system and navigational network to provide directional vector at a given place.

You have to picture a (at least I believe so) grade school easy interaction that (no surprise) has the same features of the revered “scientific method” of human behavior. Where I look for evidence of the “method” in use elsewhere I now end up back millions of years at an earlier than thought possible origin of “scientific thinking” then back in grade school with what I wish my science teachers could have said, to help me figure out what it actually is. The slide presentation would be something I have to put on my wall and spend a year or more trying to figure out the details.

Hopefully the additional information will help make sense of how the two main parts work together.