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 …

Many models of early cortical processing have shown how local learning rules can produce
efficient, sparse-distributed codes in which nodes have responses that are statistically …

How does the brain learn to recognize objects visually, and perform this difficult feat robustly
in the face of many sources of ambiguity and variability? We present a computational model …

The categorization of real world objects is often reflected in the similarity of their visual
appearances. Such categories of objects do not necessarily form disjunct sets of objects …

The hippocampal subfield CA3 is thought to function as an auto-associative network that
stores experiences as memories. Information from these experiences arrives directly from …

A key insight from 50 years of neurophysiology is that some neurons in cortex respond to
information in a highly selective manner. Why is this? We argue that selective …

Visual categorization is a remarkable ability that allows us to effortlessly identify objects and
efficiently respond to our environment. The neural mechanisms of how visual categories …

A theory is developed of how recognition categories can be learned in response to a
temporal stream of input patterns. Interactions between an attentional subsystem and an …

Myriad signals relentlessly bombard our senses. These signals may ar? rive in disconnected
pieces, yet we can integrate them as unified moments oi conscious experience. The …

How are sequences of events that are temporarily stored in a cognitive working memory
unitized, or chunked, through learning? Such sequential learning is needed by the brain in …