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<item>
  <id>01621440</id>
  <dt>j</dt>
  <an>01621440</an>
  <augroup>
    <au>Rubin, Jonathan E.</au>
  </augroup>
  <ti>Steady states in an iterative model for multiplicative spike-timing-dependent plasticity.</ti>
  <so>Netw., Comput. Neural Syst. 12, No.2, 131-140 (2001).</so>
  <py>2001</py>
  <pu>Informa Healthcare</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
    <ut>spike-timing-dependent plasticity</ut>
    <ut>neural network</ut>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1080/713663218</li>
  </ligroup>
  <abgroup>
    <ab>Summary: Recent experimental evidence suggests that synaptic plasticity depends on the precise timing of pre- and post-synaptic activity. In this paper, an iterative model for a multiplicative form of this spike-timing-dependent plasticity (mSTDP) is introduced. This model is incorporated into a neural network with many input cells coupled via excitation to a single output cell. Analysis of this network yields a criterion for the output cell to fire on every iteration, as well as general formulae for the steady-state output firing rate and the steady-state value to which all synaptic weights are driven by mSTDP. These characterize the basic state of network operation generated by mSTDP.</ab>
    <rv></rv>
  </abgroup>
</item>