An Evolutionary Framework for Replicating Neurophysiological Data with Spiking Neural Networks

  • Emily L. Rounds
  • Eric O. Scott
  • Andrew S. Alexander
  • Kenneth A. De Jong
  • Douglas A. Nitz
  • Jeffrey L. Krichmar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)

Abstract

Here we present a framework for the automatic tuning of spiking neural networks (SNNs) that utilizes an evolutionary algorithm featuring indirect encoding to achieve a drastic reduction in the dimensionality of the parameter space, combined with a GPU-accelerated SNN simulator that results in a considerable decrease in the time needed for fitness evaluation, despite the need for both a training and a testing phase. We tuned the parameters governing a learning rule called spike-timing-dependent plasticity (STDP), which was used to alter the synaptic weights of the network. We validated this framework by applying it to a case study in which synthetic neuronal firing rates were matched to electrophysiologically recorded neuronal firing rates in order to evolve network functionality. Our framework was not only able to match their firing rates, but also captured functional and behavioral aspects of the biological neuronal population, in roughly 50 generations.

Keywords

Spiking neural networks Evolutionary algorithms Indirect encoding Neurophysiological recordings Plasticity Data matching Parallel computing 

Notes

Acknowledgments

Supported by the National Science Foundation (Award IIS-1302125).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Emily L. Rounds
    • 1
  • Eric O. Scott
    • 2
  • Andrew S. Alexander
    • 3
  • Kenneth A. De Jong
    • 2
  • Douglas A. Nitz
    • 3
  • Jeffrey L. Krichmar
    • 1
  1. 1.Department of Cognitive SciencesUniversity of California, IrvineIrvineUSA
  2. 2.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA
  3. 3.Department of Cognitive ScienceUniversity of California, San DiegoLa JollaUSA

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