Neural-network models of learning and memory: leading questions and an emerging framework

Author(s): Carpenter, G.A. |

Year: 2001

Citation: TRENDS in Cognitive Sciences, Vol.5 No.3 March 2001.

Abstract: Real-time neural-network models provide a conceptual framework for formulating questions about the nature of cognition, an architectural framework for mapping cognitive functions to brain regions, a semantic framework for defining terms, and a computational framework for testing hypotheses. This article considers key questions about how a physical system might simultaneously support one-trial learning and lifetime memories, in the context of neural models that test possible solutions to the problems posed. Model properties point to partial answers, and model limitations lead to new questions. Placing individual system components in the context of a unified real-time network allows analysis to move from the level of neural processes, including learning laws and rules of synaptic transmission, to cognitive processes, including attention and consciousness.

Topics: Biological Learning, Machine Learning, Models: ARTMAP, Distributed ART,

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