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Modeling networks with linear (VLSI) integrate-and-fire neurons

  • Maurizio Mattia
  • Stefano Fusi
Part I: Coding and Learning in Biology
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

We analyse in detail the statistical properties of a “canonical” integrate-and-fire neuron with a linear integrator as often used in VLSI implementations [1]. We show that a network of such elements can maintain both stable spontaneous activity and selective (stimulus specific) activity, contrary to current opinion. The spike statistics appears to be qualitatively the same as in networks of conventional (exponential) integrate-and-fire neurons that in turn, exhibit a wide variety of characteristics observed in cortical recordings[2].

Keywords

Input Current Synaptic Efficacy VLSI Implementation Stable Fixed Point Negative Drift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Maurizio Mattia
    • 1
  • Stefano Fusi
    • 1
  1. 1.INFN, Sezione di Roma 1, Dipartimento di FisicaUniversità di RomaRomeItaly

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