Deep Predictive Learning: A Comprehensive Model of Three Visual Streams


A very recent competing / complementary model of deep predictive coding in the brain.

O’Reilly, Randall C., Dean R. Wyatte, and John Rohrlich. “Deep Predictive Learning: A Comprehensive Model of Three Visual Streams.”


How does the neocortex learn and develop the foundations of all our high-level cognitive abilities? We present a comprehensive framework spanning biological, computational, and cognitive levels, with a clear theoretical continuity between levels, providing a coherent answer directly supported by extensive data at each level. Learning is based on making predictions about what the senses will report at 100 msec (alpha frequency) intervals, and adapting synaptic weights to improve prediction accuracy. The pulvinar nucleus of the thalamus serves as a projection screen upon which predictions are generated, through deep-layer 6 corticothalamic inputs from multiple brain areas and levels of abstraction. The sparse driving inputs from layer 5 intrinsic bursting neurons provide the target signal, and the temporal difference between it and the prediction reverberates throughout the cortex, driving synaptic changes that approximate error backpropagation, using only local activation signals in equations derived directly from a detailed biophysical model. In vision, predictive learning requires a carefully-organized developmental progression and anatomical organization of three pathways (What, Where, and What * Where), according to two central principles: top-down input from compact, high-level, abstract representations is essential for accurate prediction of low-level sensory inputs; and the collective, low-level prediction error must be progressively and opportunistically partitioned to enable extraction of separable factors that drive the learning of further high-level abstractions. Our model self-organized systematic invariant object representations of 100 different objects from simple movies, accounts for a wide range of data, and makes many testable predictions.

The authors note similarity to an older incarnation of HTM:

Hawkins’ Model
The importance of predictive learning and temporal context are central to the theory advanced by Jeff Hawkins (Hawkins & Blakeslee, 2004). This theoretical framework has been implemented in various ways, and mapped onto the neocortex (George & Hawkins, 2009). In one incarnation, the model is similar to the Bayesian generative models described above, and many of the same issues apply (e.g., this model predicts explicit error coding neurons, among a variety of other response types). Another more recent incarnation diverges from the Bayesian framework, and adopts various heuristic mechanisms for constructing temporal context representations and performing inference and learning. We think our model provides a computationally more powerful mechanism for learning how to use temporal context information, and learning in general, based on error-driven learning mechanisms. At the biological level, the two frameworks appear to make a number of distinctive predictions that could be explicitly tested, although enumerating these is beyond the scope of this paper.

Are there similar attempts to understand the neocortex on a high level?
Free computational neuron models
What happens in layers 5a and 6a?
What are the flaws in Jeff Hawkins's AI framework?
SDR theoretical properties and HTM
Why is HTM Ignored by Google DeepMind?
Is the neocortex only a pattern recognizer?
Numenta turns attention to The Thalamus!
Not Oscillations Traveling Waves
Can the brain do backprop?
Retrieving things stored in memory in HTM
Esperanto NLP using HTM and my findings
Hex Grids & 1000 Brains Theory

I do wish they would have provided reasoning for this claim:

We think our model provides a computationally more powerful mechanism for learning how to use temporal context information, and learning in general, based on error-driven learning mechanisms.


I am reading this in the context of my “dumb boss, smart advisor” model and I have to say - it’s sending shivers down my spine.

One of the parts that the authors point out as needing more work is the source of the high-level training patterns to generate the seed errors.

If you assume that the older lizard brain is going about its normal behavior in a mewling infant - looking, feeling, tasting, and living in general - and the cortex is getting this as the higher order input for the pattern to seed training - the explanations match up very nicely.


Does the learning model in this paper include the HTM Neuron? Meaning is there a predictive state (dendritic spike modeling)?



Very similar, perhaps enough to use it directly. Since is related to the phase between the prediction and update inside a single wave the order of evaluation would serve much the same function.
It also works with a scanning pattern that is similar to the biological model that convolving tries to emulate.
This may well be the first “killer app” that deep learning HTM nay-sayers need to see to be shown that the biologically based model is as capable as the applications that statistically based point neurons are typically used for. It learns in an “unsupervised” manner in a few hundred presentations, not epochs of thousands. And without the forms of back-prop that I think everyone can agree is somewhat of a crutch.

I posted something to Jeff recently that ties in with this:

Please note that this also includes some of the oscillatory/phase involvement we were touching on in a different thread:

In this you stated that your research is looking into phase related processing. This model has it in spades.

Lastly - they mention in passing that this same general system would be applicable to sensorimotor systems. When you get the overall scheme it does seem extensible.

How extensible?

I am struggling to see how one could combine the coritical-IO system with speech hearing & production using this general approach. It may take some time to work this out but I will be mulling to see if it could make sense.

Emotional coloring from the amygdala seems to be an important feature in what has to be stored in the word sequence-grammar & word store.

What goes on in the lizard brain grows every-more important to understanding the cortex.


This paper adds some powerful support to the proposed “three visual streams” model.

Now if I can find some papers supporting the proposed plus/minus-phase temporal learning mechanism …

Retrieving things stored in memory in HTM