[Comp-neuro] Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world

Stephen Grossberg steve at cns.bu.edu
Mon Oct 8 14:20:48 CEST 2012


The following review article about Adaptive Resonance Theory can now be downloaded from http://cns.bu.edu/~steve. It is the most comprehensive heuristic review of ART as a cognitive and neural theory that I have ever written.

Grossberg, S. (2012). Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world. To appear in the 25th anniversary special issue of Neural Networks.

Abstract
Adaptive Resonance Theory, or ART, is a cognitive and neural theory of how the brain autonomously learns to categorize, recognize, and predict objects and events in a changing world. This article reviews classical and recent developments of ART, and provides a synthesis of concepts, principles, mechanisms, architectures, and the interdisciplinary data bases that they have helped to explain and predict. The review illustrates that ART is currently the most highly developed cognitive and neural theory available, with the broadest explanatory and predictive range. Central to ART's predictive power is its ability to carry out fast, incremental, and stable unsupervised and supervised learning in response to a changing world. ART specifies mechanistic links between processes of consciousness, learning, expectation, attention, resonance, and synchrony during both unsupervised and supervised learning. ART provides functional and mechanistic explanations of such diverse topics as laminar cortical circuitry; invariant object and scenic gist learning and recognition; prototype, surface, and boundary attention; gamma and beta oscillations; learning of entorhinal grid cells and hippocampal place cells; computation of homologous spatial and temporal mechanisms in the entorhinal-hippocampal system; vigilance breakdowns during autism and medial temporal amnesia; cognitive-emotional interactions that focus attention on valued objects in an adaptively timed way; item-order-rank working memories and learned list chunks for the planning and control of sequences of linguistic, spatial, and motor information; conscious speech percepts that are influenced by future context; auditory streaming in noise during source segregation; and speaker normalization. Brain regions that are functionally described include visual and auditory neocortex; specific and nonspecific thalamic nuclei; inferotemporal, parietal, prefrontal, entorhinal, hippocampal, parahippocampal, perirhinal, and motor cortices; frontal eye fields; supplementary eye fields; amygdala; basal ganglia: cerebellum; and superior colliculus. Due to the complementary organization of the brain, ART does not describe many spatial and motor behaviors whose matching and learning laws differ from those of ART. ART algorithms for engineering and technology are listed, as are comparisons with other types of models. 


Stephen Grossberg
Wang Professor of Cognitive and Neural Systems
Professor of Mathematics, Psychology, and Biomedical Engineering
Director, Center for Adaptive Systems http://www.cns.bu.edu/about/cas.html
http://cns.bu.edu/~steve
steve at bu.edu



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