Most of you have probably not heard of Stephen Grossberg. He is in my opinion one of the greatest neuroscientists of this generation. Maybe the greatest theoretical neuroscientist ever.
Neuroscience is swimming (barely) in a ton of data, but very little in the way of a unifying framework. Steve's body of work over 60 years provides that. ART is only one part of it. ART has some way to go before proving itself as undeniably useful as deep-learning on the application side of things. But it is very compelling as a model for how our (mammalian) brains might be operating.
One fundamental idea, which is worth considering in applications is how almost all of deep-learning is founded on the idea of an "error" (A mis-match between expectation and the prediction) and how this flows through the system. Backprop is obviously the most popularly known method for this flow. ART is fundamentally different in that it works with the idea of a match rather than a mis-match. This foundational even philosophical difference is interesting because if allows for learning to happen all the time even while perceiving is simultaneously happening. It also allows for learning to be unsupervised. This philosophical departure point is actually very interesting: If you rely entirely on the idea of an error and throw out the rest, can you even hope to build a conscious system? Steve btw also has what I think is the most elegant and mechanistically precise explanation of consciousness, which builds on ART. Even if you are skeptical of the current promise of ART as a replacement for deep-learning, his explanation of how ART-like systems enable consciousness (and different conscious percepts) are alone worth the price of admission.
I’ve written more about Stephen Grossberg and his book here: https://saigaddam.medium.com/the-greatest-neuroscientist-you...