Neural coding:Rate/Temporal coding vs. Sparse coding

The frequency does not accumulate with higher order maps. Rate coding sounds about the same anywhere you look in the cortex. The phase coding (leading edge) of the pulse train seems to be important in the competition phase of inhibition competition. Once the winning cell is established the rate coding comes into play to signal value.

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Frequency meaning average input accumulation time, or output rate?


Dendrites accumulate the equivalent of charge; they can respond to both a lot of pulses along the dendrite or fewer faster pulses.

The output of a cell varies with the degree of excitement it receives on the dendrites.

Hence the hideous complexity of spike timing neural models.

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Well, the output burst frequency may be fixed, but accumulation time depends on gating on axonal hillock, maybe some other places. Is that also fixed, or maybe increasing with elevation?
Also, output time frame may increase with lateral reverberation.

Frequencies are NOT fixed.

Can you be more clear about what you might mean by elevation?
If you are referring to location in the hierarchy then no - the rate coding is a basic property of pyramidal neurons.
As you pull back and look at map-2-map communications past the primary receptive fields this basic cell response rates are harnessed on a very large scale. The interaction with the thalamus coordinates this into the large scale activity that we think of as brain waves. I have talked about in various posts on this forum.

The basic rate is about 10 Hz when the cell are driven to synchronized coding; this is the same everywhere in the cortex. As you get into the large coordinated actions there are higher frequencies such as how I believe gamma is used to mediate inhibitory interaction mechanisms.

Yes. I know that neurons are pretty much the same in all areas, but from what I recollect the size of ensembles is greater in association areas. Which would allow / require greater duration of lateral inhibition and reverberation. You mentioned a bunch of counter-examples, but aren’t they all in primary cortices?

Here is a textbook thing: Joaquin Fuster in Cortex & Mind, p. 73: “At the lower level, representation is highly concrete and localized, and thus highly vulnerable. Local damage leads to well-delimited sensory deficit. In unimodal association cortex, representation is more categorical and more distributed, in networks that span relatively large sections of that cortex… In transmodal areas representation is even more widely distributed… P. 82: “Thus a higher-level cognit (e.g., an abstract concept) would be represented in a wide network of association cortex…”

Spatial aspects.
Unrelated to timing.


Rate coding could add more information to HTM, but we are not actively investigating it. We think there is a whole lot you can do without rate coding.


I have to remind you that this is not a question about how HTM works and what can be done with HTM. This is a “General Neuroscience” topic.
Any comment on neural coding?


By the way - while you state rather authoritatively that retinal cells don’t spike you may wish to reconsider that statement:

As you can see in these papers, the spike train encodes considerable information in the structure of the pulse spacing and phase/latency relative to other cell outputs.

Ok. I got it first-hand from Tomaso Poggio, figured he is pretty authoritative. Maybe that was about lateral connections within retina.
BTW, Joaquin Fuster talks about reverberation within ensembles (his cognits) do you know anything about that?

I have been following various lines on that since reading Grossberg and his ART models and things like synfire chains.

This is all conflated with the brainwave/timekeeping actions of the thalamus and the spreading of activity as touched on lightly in JHs talk today.

So - which particular aspect are you interested in?

The temporal aspect that we discussed. If higher-area ensembles are larger and wider, then they will most likely reverberate longer. Not always, but there will be greater variation in duration.

Link to a paper?

Sorry, no links, just quotes from “How to Create a Mind” by Ray Kurzweil, p. 86: A study of 25 visual and multi-modal cortical areas by Daniel Felleman found that “As they went up the neocortical hierarchy,… processing of patterns comprised larger spatial areas and involved longer time periods“.
Another study by Uri Hasson stated: “It is well established that neurons along the visual cortical pathways have increasingly larger spatial receptive field.” and found that “similar to cortical hierarchy of spatial receptive fields, there is a hierarchy of progressively longer temporal receptive windows”.

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Don’t you think it’s reasonable assumption that longer reverberation would be necessary to form and maintain larger ensembles?

I don’t think that is a given. I do think that the data stream is just that - a stream. The processing is guided with feedback from the higher levels acting as a filter but it is still a stream. I see the WHAT and WHERE streams as just that - streams that flow up the hierarchy to the association region(s).

There can be more stable representations as you ascend the hierarchy; this representation can be the stabilizing feedback. I like to think of the stream as “bunching up” as it ascends the hierarchy. I suppose that you could think of this as some sort of ensemble but I think that misses the essential peristaltic streaming nature of the processing.

The high level representations are the basic data interchange between the hubs.

What I like about this basic approach is I can see how it forms and develops from a “empty” structure to a fully trained one. I find the method outline in the " Deep Predictive Learning" particularly appealing in this regard. This development and self-organization is missing in many of the models I have looked at.

I don’t see a contradiction here. Yes, it’s a stream, but there is incremental filtering along the way. To overcome this filtering, representations must be increasingly stable / invariant, both spatially and temporally. That means they need larger receptive field, with feedback to maintain connections while the weights are trained.

You may wish to think about what you get with my hex-grids or Numenta’s 1000 brain lateral connections. Both are an inherently peristaltic streams without the usual crutch of fanning in or out of connections.

Both are compatible with maps cross-connecting or level skipping as the stream ascends the hierarchy. This gets your larger assemblies in a biological plausible way.

JH also touched on this in today’s talk.

I have become invested in the concept that the streams stay mostly parallel all the way up the hierarchy. So far it has been possible to cast common tasks into this model. Some of the solutions take a radical rethink of how the brain does things - it’s not at all the way that one might do it using a stored program computer. So far the biggest win has been how well this model solves the visual palimpsest problem - layers of image fragments combining into recognition of an object.

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I am going to build a neural network model according to my ideas. If anything interesting happens, I will put it here.