How is your proposal different from Hebb's rule? I remember reading in the
1970s as a teenager about how neurons represent mental concepts and
activate or inhibit each other through 2 kinds of synapses. I had the idea
that synapses would change states in the process of forming memories. At
the time it was unknown that synapses would do this. In 1980 I described
this model of classical conditioning in my freshman psychology class. I got
a B on the paper. Years later I learned that Hebb proposed the same idea in
1949.

Connectionism is a simple idea that makes it easy to understand how brains
work by learning associations between concepts, but it lacks a mechanism
for adding new concepts. That problem is solved by representing concepts as
linear combinations of neurons, but it makes a neural network more like a
black box of inscrutable matrices.

Your diagram shows a feed forward network, but in reality there are
connections going in all directions. Lateral inhibition within the same
layer gives you a winner take all network as the mechanism for attention in
a transformer. Positive feedback loops give you short term memory. Negative
feedback gives you logarithmic scaling of sensory perceptions.

On Wed, Oct 18, 2023, 10:14 AM Danko Nikolic <danko.niko...@gmail.com>
wrote:

> Here is my proposal on what connectionism is missing in order to reach a
> 'true" AI i.e., an AI that is much more similar to how the human brain
> works.
>
> This is a nine-minute video on the secret that was missing:
> https://www.youtube.com/watch?v=GVW6H4iCsTg&ab_channel=dankonikolic
>
> I hope it is clear enough.
>
> Comments and questions are welcome.
>
> Danko
>
>
> Dr. Danko Nikolić
> www.danko-nikolic.com
> https://www.linkedin.com/in/danko-nikolic/
> -- I wonder, how is the brain able to generate insight? --
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