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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5
Synaptic Depression in Associative Memory Networks
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
Dmitri Bibitchkov, Max-Planck-Institut f?r Str?mungsforschung
J. Michael Herrmann, Max-Planck-Institut f?r Str?mungsforschung
Theo Geisel, Max-Planck-Institut f?r Str?mungsforschung
We analyze the effects of synaptic depression on the stability of patterns stored in neural networks with low activity level. Applying mean-field theory, we show that the stationary states remain unaffected by the synaptic depression. However, the stability of memory patterns changes drastically causing a reduction of memory capacity. Further, it is demonstrated and confirmed by numerical calculations that the sensitivity of the network to input changes is enhanced.
Citation:
Dmitri Bibitchkov, J. Michael Herrmann, Theo Geisel, "Synaptic Depression in Associative Memory Networks," ijcnn, vol. 5, pp.5050, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000
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