DocumentCode
353304
Title
Synaptic depression in associative memory networks
Author
Bibitchkov, Dmitri ; Herrmann, J. Michael ; Geisel, Theo
Author_Institution
Max-Planck-Inst. fur Stromungsforschung, Gottingen, Germany
Volume
5
fYear
2000
fDate
2000
Firstpage
50
Abstract
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
Keywords
content-addressable storage; dynamics; neural nets; associative memory networks; mean-field theory; memory capacity; memory patterns; stationary states; stored patterns; synaptic depression; Associative memory; Biological system modeling; Intelligent networks; Neural networks; Neurons; Neurotransmitters; Pattern analysis; Production; Stability analysis; Stationary state;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.861434
Filename
861434
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