[tonymillion]

This kind of question is meaningless, and I’m pretty sure it comes from the old view of neurons being somewhat akin to a 1-1 or many-1 digital switch (it isn’t).

In fact, we currently don’t even know the full extent of the types of neurons in the brain or the way they function and interact [1]

There are neurons that only fire when a certain percentage of their inputs are stimulated, some that have a temporal component to their firing and more.

In all the computer neural net research I read I never saw an implementation that truly explored or implemented more than a few types (like 3-4) of neuronal input and only one type of output

[1] https://qbi.uq.edu.au/brain/brain-anatomy/types-neurons

[mjan22640]

I dont think this is a problem. The computer nrural networks are closer to a single biological neuron than to a biological neural network. That provides tighter coupling. Long distance generalized "connections" can materialize as weight chains, recurence can provide timing.

[agumonkey]

I think there's gonna be a new wave of idea, I see more and more concepts related to distributed / massive parts related through their topologies / tissue like structures.

[ethn]

Yes Walter Pitt's proposal, which he disproved. But we know the prefrontal cortex is capable of simulating a turing machine, or any machine for that matter.