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

@inproceedings{Pfister2006OptimalSD,
  title={Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Learing},
  author={Jean-Pascal Pfister and Taro Toyoizumi and David Barber and Wulfram Gerstner},
  year={2006}
}
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 via gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up and down regulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. If the presynaptic spike arrives before… CONTINUE READING