Deep Predictive Learning: A Comprehensive Model of Three Visual Streams

I guess we disagree on that. It should be easy. Otherwise, a minor change in the operating conditions will make it fall apart. Robustness and flexibility comes from simplicity.

My best analogy is a modern processor: if you take it apart, it looks extremely complex with many billions of transistors. But the basic architecture (storage-program) can be explained in a couple of sentences. You need all those transistors to solve an “engineering” problem. Mainly memory and bandwidth walls. You never will be able to understand from the latest IBM Power9 (with 8 B transistors) how storage-program works. It’s much better Numenta approach: try to replicate how Power9 behaves by connecting sparse pieces of knowledge together.

If we are arguing just to score points then you win. The brain can be described in a child’s picture book.

I’m not sure if that level of description will allow you to build an entire visual learning system but - yes - there should always be a simple overview. As a teacher of technical topics I will agree with you that the Feynman principle should apply: “If you can’t explain something in simple terms, you don’t understand it”

I was assuming that forum members are the ones that are digging into the 8 B transistor level models to make things work and are still working out how to pull this off. At this level, the explanations can get complicated. As you well know, elsewhere on the forum they are hashing out how to do branch prediction and while the basics of a CPU instruction execution unit are conceptually straight forward - tweaks to make it work better are proving to be less so.

No … just to learn a bit more :slight_smile:

I know something about that :slight_smile: I’ve worked on cache coherence… although at first sight is really complex, the basic principles and why something will work or something won’t, isn’t that hard. ( Computer architecture is half based on intuition/experience half on engineering ).

It’s been 40 years since I did bit-slice level CPU design; I made a nice little 8/16-bit CPU based on the 74170/74181/74182/74200 chips. Microcode used to be chip logic and not Verilog code. FPGAs were not really a thing yet. Things have gotten much more complicated since then.

That said - do you have a pointer to different papers that do deep predictive learning?
As far as I know - this is an emergent property of a “larger” multi-layered processing system.

I have been a long-time fan of the Global workspace theory, in particular the work of Stanislas Dehaene. This is also a large scale model of interconnected maps. There are also interactions between the counter-flowing streams that drive much of the interesting behavior in these systems. I see this emergent behavior as a recurring theme in larger systems.

My focus in all this is more oriented to the larger system level engineering and how the maps have to work together. The HTM model is surely part of this on one end of the size scale. The engineering scale at this level covers several orders of magnitude. You have to consider everything from individual synapses to large scale fiber tracts and population densities of fiber projections.

I could be way off base but I do think it’s time to take off the training wheels and put the HTM model in play as part of larger systems. I expect that this will change some of the current notions of what various parts are doing and refine the model.

In particular, I expect that the focus will shift from object representation to communications of object representation and deeper consideration of the distributed nature of those representations. I also expect that the coordination functions of cortical waves will take on more importance than it currently holds in the HTM canon.

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Noup… sorry but I don’t.

The “theater metaphor” is nice.

Indeed… we need to advance, but step-by-step.

The reading is not easy, but this “3 visual streams” paper combines lots of interesting ideas about L4/L5/L6/thalamus functions, temporal learning, error coding, alpha oscillation, top-down shortcuts increasing the learning rate, sequential development of the visual pathways (“where”, “where & what”, then “what”).

Combined with other readings, it helps me to conceptualize a different potential explanation of cortico-thalamic interactions (mainly based on an action-based interpretation of perception, and on cortical efferent copies to the thalamus). I’ll post about it when I will have formalized the mess in my mind.

Concerning the paper, I would like to mention some key points that would deserve a discussion:


The thalamus has a double function: attention (not studied in the paper) and cortical learning (the main focus of the paper). The authors take the visual cortex to explain their theory, but it could be generalized to other areas as well.


There are two kinds of learning:

  • Self-organizing learning which extracts statistical regularities (like classic auto-encoders). This is the kind of learning is used to create our internal models of our body and the environment.
  • Error-driven learning which leverages differences between expectations and outcomes. This is the kind of learning is used to shape our “alpha-long” predictive abilities (the term is mine) and our longer-term reward system.

In predictive learning, the learning could be both self-orginizing and error-driven.
At the beginning of development, cortical areas learn slowly and independently in this manner, until some “fast-learning” cortical areas could then help the other ones by providing top-down inputs for more efficient error-driven learning (more on this later).

Learning over alpha oscillations

Let’s focus on the “alpha-long” predictive ability of the neocortex.
By “alpha-long”, I mean the very short-term predictions of what will be experienced in the next 100ms (alpha oscillation).

The alpha oscillation creates two timeframes:

  • A 75ms “minus-phase” (= 3 gamma ticks) where the deep layers are isolated from L4 inputs to allow the computation of a pure prediction in L5/L6
  • A 25ms “plus-phase” (= 1 gamma tick) where the current state of the environment and the ongoing internal mental state of the organism are shared with deep layers.


The error-signal is then computed in high-order nuclei of the thalamus (pulvinar in this example for vision) which receive both:

  • The current state from L5IB cells (which relay the “ground truth” signal of the current moment coming from superficial layers L2/3 & L4)
  • The prediction from L6CT cells (which is the result of an interaction with local L5IB and L6CC cells, and long distance top-down inputs from L6CC).

Importantly, this error-signal is encoded by the thalamus as a STDP-dedicated temporal differences so that the cortex knows what synaptic weights to increase/decrease. The Spike Timing Dependent Plasticity (STDP) is a biologically-supported learning process allowing local backpropagation of errors to neighboring neurons.

Getting around the credit assignment problem with development phases

However, we have to keep in mind that an error could have several causes originating either from the given area, from other areas, or from both!

If each cortical areas correct the synaptic weights in parallel, we could end with a non-convergent system where connected areas iteratively adapt and unadapt their weights given the reciprocal changes. That would explain why we have critical periods in brain development.

The authors take the example of the different visual pathways:

  1. First, the “where” pathway can learn pretty easily on its own
  2. Then, it helps the development of an hypothesized “where & what” intermediate pathway thanks to top-down inputs from the stabilized “where” pathway
  3. Finally, the “what” pathway can construct complex spatially-invariant object representations


Overall, their explanations and their computer implementation are full of smart ideas that look very promising. And their model would fit well on top of a L2/3 object representation model (for instance Bitking’s hexgrid or other).

My doubts

However, I still have some reserves on my side. Mainly on those two points:

  • Given my other readings, I don’t buy their interpretation of L6CT cells. For now, I prefer to stick to Sherman & Guillery’s view of a modulation role of L6 cortico-thalamic cells. I may have a different view that could reconcile those two views, but I need to take more time to think about it.
  • It is not yet clear for me how the error-signal from higher areas is adapted to be understandable by lower areas, did someone get it?

If you want further reading about the paper:

Still thinking about it!


I agree - the paper is the culmination of many other papers where they pulled many of the projects they have been working on into a coherent whole. Many papers talk about a single point in great depth - the 3VS paper has has a bunch of ideas that are all inter-related. They demonstrate a model to test the ideas that is one of the most ambitious that I have seen - it encompasses most of the visual processing stream.

If I had to pick out the most important idea it would be the use of top down and bottom up streams as a mechanism to radically enhance learning. This fits well with my understanding of the relationship between the lizard brain and the cortex. The lizard brain has a small amount of hardwired learning that is fed into the system from one end, and the real world from the other end. The lizard brain contribution acts as a lighthouse to constrain the search space. I see this, combined with the ground truth from the senses, as a biologically plausible replacement for back propagation.

As @mthiboust pointed out, the model is based on the leabra algorithm, but the ideas about structural organization should be generally usable with other predictive implementations. It describes what is necessary to incorporate the organization role of the thalamus into the counter-flowing streams to gain the advantages normally thought to be the realm of back-propagation.

The model presented does take thousands of hours of computer time to train. I think that this is due to the use of the Leabra neurons; the training is using essentially straight Hebbian learning rules. As I see it - if it had been built using HTM neurons it would have trained up much faster.

One of the ideas I keep coming back to is the possibility of two predictive mechanisms at work at the same time - the Deep Leabra in the feedback direction in L6 and HTM in the feedforward direction in L5.

Replacing the simple “top level” drive with a simple lizard brain would be a logical extension.


When you say feedback vs feedforward, do you mean error-driven vs self-organizing learning as mentioned in my post ?

Because I think that the Deep Leabra is doing both at the same time.

I strongly agree that Deep Leabra does learning in both directions.

I am saying that the proposed Deep Leabra model may work much better with the HTM model substituted in the relevant part of the model.

And of course, as you mentioned, the hex-grid organization dropped into place where it belongs.

The brain has many learning systems.

The Deep Leabra model is essentially based on Hebbian spike timing learning. This is a very slow method; the one-shot feature of HTM is a huge improvement.

Likewise, the attention/activation gating from the RAC that I have mentioned several times before would serve to speed up the learning in other ways. An example:

Lastly - one more concept to work in as you think about what is going on with the 3VS paper: the concept of the global workspace. If the stream from the lizard brain could be thought of as a need state then a match to the sensed stream should be considered as an important event.


I think feedforward means from lower brain levels to higher levels and feedback the other way around, which is meant to speed up learning in the different brain areas (e.g. a higher-level cue via language input can enhance lower-level features such as recognizing a figure in a cluttered image). In Leabra, error-driven learning is indeed balanced with Hebbian learning and both happen at the same time.


In a private discussion, Randall O’Reilly (the first author of the 3VS paper) responded in details to some of my doubts expressed in a previous post in this thread:

I guess that some of you will be interested (he allowed me to post his explanations here).

In brief:

  • His team is about to post soon a new preprint on Arxiv about a version that works directly on 3D object images
  • Awake vs anesthetized states matter a lot when interpreting experimental results
  • Transmission of the error-signal to higher areas only

I hope it will help to clarify some points and to foster ideas.
On my side, I need a couple of days to fully digest this answer before commenting on it.


I was under the impression that the diversity of the inhibitory cells could be sufficient, in itself, to explain theoretically (short of a proof) any of those distinctive frequencies… (combined influences of networks of resonators vs integrators, and so on). I know you studied some of the wavy stuff… do you have an opinion on this?

I was also surprised by his explanation that doesn’t involve inhibitory cells. But nope, I don’t really have an opinion on this currently.

In fact, I am still trying to fully understand his answer.

This is the 3rd time I’ve tried reading this paper (this time over holiday “break”) and I must say it is not an easy read. Red is stuff I don’t understand yet.

I’m 6 pages into 50+ pages. No pictures yet.


It is the wrap-up of at least half a dozen prior papers and if you have not absorbed them first AND been reasonably familiar with the layout of both the thalamus and visual system - well - all they give is pointers and bread crumbs to the prior material.
I have been following their work and have a nodding familiarity with the referenced material and it was still pretty dense for me. That said, it does show how ALL of that comes together in a tour de force!
It really is worth the work.

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If you get NOTHING else from this - note that they use the counter-flowing information channels to do some of what back-prop does AND dramatically constrain the degrees of freedom in learning. This speeds up hierarchical learning something wonderful. Unlike normal “random” exploration of the hierarchical search space, the learning direction has a direction. Since this is the prodding of the (sorry Dr Cisek) lizard brain it is guided to the most useful functions to support the dumb boss.


Minus/plus - breaks up every alpha cycle.

This is the core of the 3VS prediction model.

It is a very different prediction method from HTM but very viable and well supported in biology. The slow response of the lower layer is well documented.

  • HTM does prediction from the last cycle to the current cycle using depression of firing potential and through that, winning the column election;
  • the minus/plus thing does it within the cycle between the layers and is used to provide a match between prediction and ground truth, every single cycle. The action is local to the cell.

I see the two methods as complimentary.


Do I need to go deeper and try understanding the DeepLeabra algorithm (O’Reilly 2015)? Or can I think of that as a black box?

I did but you can get a lot put out the 3VS paper without it. I would go this the BB path unless it seems to be getting in the way of understanding.

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Here is an open question I keep coming back to. @Bitking Maybe you can help.

This connection from L2/3 to L5…

What is this? I understand the 5IB FF connections to the thalamus, but this intracortical connection confuses me. My model has lateral connections between CCs in L2/3 to L2/3, and more in L5 to L5. It must be a link to the past, correct? The previous state of V2 Super, but how does it get to the deep layers?

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