Using Firing-Rate Dynamics to Train Recurrent Networks of Spiking Model Neurons

  title={Using Firing-Rate Dynamics to Train Recurrent Networks of Spiking Model Neurons},
  author={Brian DePasquale and Mark M. Churchland and L. F. Abbott},
Recurrent neural networks are powerful tools for understanding and modeling computation and representation by populations of neurons. Continuous-variable or “rate” model networks have been analyzed and applied extensively for these purposes. However, neurons fire action potentials, and the discrete nature of spiking is an important feature of neural circuit dynamics. Despite significant advances, training recurrently connected spiking neural networks remains a challenge. We present a procedure… CONTINUE READING
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