Friday, November 2, 2012

Adaptive resonance theory: How a brain learns to consciously attend, learn, and recognize a changing world IV

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:
"Figure 6: The SMART model clarifies how laminar neocortical circuits in multiple cortical areas interact with specific and nonspecific thalamic nuclei to regulate learning on multiple organizational levels, ranging from spikes to cognitive dynamics. The thalamus is subdivided into specific first-order and second-order nuclei, nonspecific nucleus, and thalamic reticular nucleus (TRN). The first-order thalamic matrix cells (shown as an open ring) provide nonspecific excitatory priming to layer 1 in response to bottom-up input, priming layer 5 cells and allowing them to respond to layer 2/3 input. This allows layer 5 to close the intracortical loop and activate the pulvinar (PULV). V1 layer 4 receives inputs from two parallel bottom-up thalamocortical pathways: a direct LGN→4 excitatory input, and a 6I→4 modulatory on-center, off-surround network that contrast-normalizes the pattern of layer 4 activation via the recurrent 4→2/3→5→6I→4 loop. V1 activates the bottom-up V1→V2 corticocortical pathways from V1 layer 2/3 to V2 layers 6I and 4, as well as the bottom-up cortico thalamocortical pathway from V1 layer 5 to the PULV, which projects to V2 layers 6I and 4. In V2, as in V1, the layer 6I→4 pathway provides divisive contrast normalization to V2 layer 4 cells. Corticocortical feedback from V2 layer 6II reaches V1 layer 1, where it activates apical dendrites of layer 5 cells. Layer 5 cells, in turn, activate the modulatory 6I→4 pathway in V1, which projects a V1 top-down expectation to the LGN. TRN cells of the two thalamic sectors are linked via gap junctions, which synchronize activation across the two thalamocortical sectors when processing bottom-up stimuli. The nonspecific thalamic nucleus receives convergent bottom-up excitatory input from specific thalamic nuclei and inhibition from the TRN, and projects to layer 1 of the laminar cortical circuit, where it regulates mismatch-activated reset and hypothesis testing in the cortical circuit. Corticocortical feedback connections from layer 6II of the higher cortical area terminate in layer 1 of the lower cortical area, whereas corticothalamic feedback from layer 6II terminates in its specific thalamus and on the TRN. This corticothalamic feedback is matched against bottom-up input in the specific thalamus. [Reprinted with permission from Grossberg and Versace (2008).]"

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
environment."

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...