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

Grossberg, S. (2012) Adaptive resonance theory: How a brain learns to consciously attend, learn, and recognize a changing world. Neural Networks.

Starting back with section 9.

He links consciousness to resonance by the idea that the "contents" of the experience - conscious qualia, are linked through the "symbolic", compressed representation. The feedback in brain/ART binds the pixels and the symbols together, which is the basis of a conscious experience.

Learning occurs in the resonant state. When their is resonance the bottom-up adaptive filter and the top-down expectation pathways have learning activated. (the weights going up and down the hierarchy).

match learning causes gamma (ergo gamma is consciousness), mismatch/reset leads to beta oscillations.

Attentional system knows how inputs are categorized, but not whether categorization is correct, orienting system knows whether categorization is correct, but not what is being categorized. This means orienting system's activation needs to be nonspecific. Can use medium-term memory (synaptic depression) to lower chances of getting stuck in same local-minima category during search process. The self-normalizing network is essential - can act as a real-time probability distribution. Search cycle is probabilistic hypothesis testing and descision making.

ART prototypes are not averages, but the actively selected critical feature patterns upon which the top-down expectations of the category focus attention. "Vigilance" is the level of acceptable matching - low vigilance learns general categories with abstract prototypes. High vigilance forces a prototype to encode an individual examplar.

p is the vigilance parameter in figure 2. This controls how bad a match can be before search for a new category is initiated. Can control vigilance by a process of match tracking. Vigilance "tracks" the degree of match between input exemplar and matched prototype. The vigilance parameter is constantly being increased just enough to trigger a reset.

What stream learns spatially invariant object categories, where stream knows object positions and how to move. Interactions between what and where overcome these informational deficiencies. The what and where stream interact to bind view-invariant and positionally-invariant object categories.

A view-specific category of a novel object is learned and activates cells at a higher level that will become view-invariant object category as multiple view-specific categories are associated with it. As the eyes move around an object surface multiple view-specific categories are learned and associated with the emerging invariant category. An attentional shroud prevents the view-invariant category from getting reset, even while new view-specific categories are rest, as the eyes explore an object. This is done by inhibiting ITa reset mechanism.

The surface-shroud resonance is formed between surface representation (V4) and spatial attention (PPC), and focuses attention on object to be learned. When shroud collapses view-invariant category can be reset, and eyes can move to a new object.

Next is section 18, page 23.