Synaptic depression in associative memory networks

D. Bibitchkov, J. M. Herrmann, T. Geisel

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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
Original languageEnglish
Title of host publicationNeural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Number of pages6
Publication statusPublished - 2000


  • 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

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