Title :
Synaptic depression in associative memory networks
Author :
Bibitchkov, Dmitri ; Herrmann, J. Michael ; Geisel, Theo
Author_Institution :
Max-Planck-Inst. fur Stromungsforschung, Gottingen, Germany
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;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861434
