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 (Gardner-Medwin, 1976). 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.
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© 1997 Springer Science+Business Media New York
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Hirase, H., Recce, M. (1997). Interneuron Plasticity in Associative Networks. In: Bower, J.M. (eds) Computational Neuroscience. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9800-5_56
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DOI: https://doi.org/10.1007/978-1-4757-9800-5_56
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-9802-9
Online ISBN: 978-1-4757-9800-5
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