Interneuron Plasticity in Associative Networks (original) (raw)

2000

Abstract

ABSTRACT Associative network models with binary synapses are widely studied as a biologically plausible memory mechanism. These models often include a single interneuron, used to set a global threshold for a network of sparsely interconnected principal cells, and the storage capacity improves with the use of a multi-step recall process[1]. We demonstrate that the inclusion of non-saturating modifiable Hebbian synaptic weights in the projection from the interneuron to the principal cells drastically improves the performance of the network. These synaptic weights reduce the influence of the principal cells that are active in a disproportionate number of memory events. The authors prefer ORAL presentation. Category: Modeling & Simulation (and also, Theory & Analysis) Theme: Learning and Memory corresponding author Interneuron plasticity in associative networks Hajime Hirase y & Michael Recce Department of Anatomy and Developmental Biology University College London London WC1E 6BT, UK e...

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