Evolving spike-timing-dependent plasticity for single-trial learning in robots.

@article{Paolo2003EvolvingSP,
  title={Evolving spike-timing-dependent plasticity for single-trial learning in robots.},
  author={Ezequiel A Di Paolo},
  journal={Philosophical transactions. Series A, Mathematical, physical, and engineering sciences},
  year={2003},
  volume={361 1811},
  pages={2299-319}
}
Single-trial learning is studied in an evolved robot model of synaptic spike-timing-dependent plasticity (STDP). Robots must perform positive phototaxis but must learn to perform negative phototaxis in the presence of a short-lived aversive sound stimulus. STDP acts at the millisecond range and depends asymmetrically on the relative timing of pre- and post-synaptic spikes. Although it has been involved in learning models of input prediction, these models require the iterated presentation of the… CONTINUE READING

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