The brain isn't organized into layers like ANNs are. It's a general graph of neurons and cycles are probably common.
Yes, there is a lot more structure to the brain than just the neocortex - there are all the other major components (thalamus, hippocampus, etc) each with their own internal arhitecture, and then specific patterns of interconnect between them...
This all reinforces what I am saying - the brain is not just some random graph - it is a highly specific architecture.
>There is of course looping too - e.g. thalamo-cortical loop - we are not just as pass-thru reactionary LLM!
Uh-huh. But I was responding to a comment about how the brain doesn't do something analogous to back-propagation. It's starting to sound like you've contradicted me to agree with me.
It seems very widely accepted that the neocortex is a prediction machine that learns by updating itself based on sensory detection of top-down prediction failures, and with multiple layers (cortical patches) of pattern learning and prediction, there necessarily has to be some "propagation" of prediction error feedback from one layer to another, so that all layers can learn.
Now, does the brain learn in a way directly equivalent to backprop in terms of using exact error gradients or a single error function? No - presumably not, it more likely works in layered fashion with each higher level providing error feedback to the layer below, with that feedback likely just being what was expected vs what was detected (i.e. not a gradient - essentially just a difference). Of course gradients are more efficient in terms of selecting varying update step sizes, but directional would work fine too. It would also not be surprising if evolution has stumbled upon something similar to Bayesian updates in terms of how to optimally incrementally update beliefs (predictions) based on conflicting evidence.
So, that's an informed guess of how our brain is learning - up to you whether you want to regard that as analogous to backprop or not.
If you really wanted to train artificial spiking neural networks in biologically plausible fashion then you'd first need to discover/guess what that learning algorithm is, which is something that has escaped us so far. Hebbian "fire together, wire together" may be part of it, but we certainly don't have the full picture.
OTOH, it's not yet apparent whether an ANN design that more closely follows real neurons has any benefit in terms of overall function, although an async dataflow design would be a lot more efficient in terms of power usage.
By my very limited understanding of neural biology, neurons activate according to inputs that are mostly activations of other neurons. A dot product of weights and inputs (i.e. one part of matrix multiplication) together with a threshold-like function doesn't seem like a horrible way to model this. On the other hand, neurons can get a bit fancier than a linear combination of inputs, and I haven't heard anything about biological systems doing something comparable to backpropogation, but I'd like to know whether we understand enough to say for sure that they don't.