Grandmother cohorts: multiple-scale brain compression dynamics during learning of object and sequence categories

This article summarises neural models of how invariant object categories are learned under conditions where eye movements freely scan a scene, and of how list categories, or chunks, are learned in response to sequences, or lists, of events that are temporarily stored in working memory. During both kinds of category learning, distributed information is compressed by a category learning process into compact groups of cells that can selectively respond to this information. Adaptive Resonance Theory models describe these learning processes and enable, in a limiting case, winner-take-all grandmother cells to be learned, even in a single trial, and rapidly activated during recall trials by bottom-up direct access. The degree of compression can vary with the amount of contextual evidence, and a distributed network of multiple categories may be needed to represent an object or event, all computed using localist mechanisms. Such a network is called a grandmother cohort in the current article.