Grossberg, S. (2012) Adaptive resonance theory: How a brain learns to consciously attend, learn, and recognize a changing world. Neural Networks.
SMART: Synchronous Matching ART. Now getting into spiking models, STDP, acetylcholine modulation, hierarchical laminar thalamic and cortical circuit designs and their interactions, oscillations.
SMART divides thalamus up into specific first-order (LGN), specific second-order (pulvinar), nonspecific, and thalamic reticular nucleus. Here's the figure:
Wow. Yeah. Some highlights: Its like thalamus is doing a loop with layer 5 - layer 5 to pulvinar is like retina to LGN. Nonspecific thalamus is the orienting mechanism. It somehow causes a reset by going through L5-L6 pathway - and it involves the habituative transmitters in L6. Reset would be implicated by beta oscillations, a resonant match results in gamma oscillations. Vigilance is controlled by acetylcholine - vigilance promotes finer categorical seperation.
Ok, here's how he explains how learning novel environments works, with respect to beta oscillations. So the first exploration of a track does not cause much beta - this is because the top-down expectation of a novel environment is usually broadly tuned, so that resonance eventually begins. (I guess, in novel environment search procedure increases top-down excitability, eventually leading to a large number of top-down neurons being activated and associated with bottom-up states.) The top-down inputs are broadly tuned to match feature patterns. So the real beta-level learning happens while pruning the top-down expectations. Using mismatch-based reset events the categories are fine-tuned. This results in beta. So you see beta in a few more trials. (So I guess the second time a category is activated, its not activated as fully, so it may not lead to a resonance at first. But then the orienting system increases excitability, leading to a larger share of the pattern, but still not full. Then maybe you need 90% of the pattern to get a resonance, but then that 90% will get reinforced. Then next time you need only 90% of that pattern.).
"Such an inverted-U in beta power through time is thus a signature of ART category learning in any
He explains about how place cells are learned through ART from grid cells. Place cells are just categories of space. Self-organizing maps can do it, so can ART, but top-down attention is needed.
next is section 41, page 39/98...