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Stochastic synapse with short-term depression for silicon neurons

  • University of Maryland, College Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We report a stochastic dynamical synapse for VLSI spiking neural systems. The compactness of the circuit, real-time stochastic behavior, and probability tuning make it well suitable to implement stochastic synapses with variety of dynamics. The stochastic synapse implements short-term depression (STD) using a subtractive single release model. Preliminary experimental results show a good match with theoretical predictions. The output from the stochastic synapse with STD has negative autocorrelation and lower power spectral density at low frequencies which can remove the information redundancy in the input spike train. The mean transmission probability is inversely proportional to the input spike rate which has been suggested as an automatic gain control mechanism in neural systems. The silicon stochastic synapse with plasticity could potentially be a powerful addition to existing deterministic VLSI spiking neural systems.

Original languageEnglish
Title of host publicationConference Proceedings - IEEE Biomedical Circuits and Systems Conference Healthcare Technology, BiOCAS2007
Pages99-102
Number of pages4
DOIs
StatePublished - 2007
EventIEEE Biomedical Circuits and Systems Conference Healthcare Technology, BiOCAS2007 - Montreal, QC, Canada
Duration: Nov 27 2007Nov 30 2007

Publication series

NameConference Proceedings - IEEE Biomedical Circuits and Systems Conference Healthcare Technology, BiOCAS2007

Conference

ConferenceIEEE Biomedical Circuits and Systems Conference Healthcare Technology, BiOCAS2007
Country/TerritoryCanada
CityMontreal, QC
Period11/27/0711/30/07

Keywords

  • Short-term depression (STD)
  • Spike
  • Stochastic synapse
  • Synaptic plasticity
  • Synaptic transmission

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