Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning

@article{Pfister2006OptimalSP,
  title={Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning},
  author={Jean-Pascal Pfister and Taro Toyoizumi and David Barber and Wulfram Gerstner},
  journal={Neural Computation},
  year={2006},
  volume={18},
  pages={1318-1348}
}
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up- and downregulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. If the presynaptic spike arrives before… 
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