Indirect training of a spiking neural network for flight control via spike-timing-dependent synaptic plasticity

@article{Foderaro2010IndirectTO,
  title={Indirect training of a spiking neural network for flight control via spike-timing-dependent synaptic plasticity},
  author={Greg Foderaro and Craig S. Henriquez and Silvia Ferrari},
  journal={49th IEEE Conference on Decision and Control (CDC)},
  year={2010},
  pages={911-917}
}
Recently, spiking neural networks (SNNs) have been shown capable of approximating the dynamics of biological neuronal networks, and of being trainable by biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity. Numerical simulations also support the possibility that they may possess universal function approximation abilities. However the effectiveness of training algorithms to date is far inferior to those of other artificial neural networks. Moreover… CONTINUE READING

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