Implementation of a Minimal Recurrent Spiking Neural Network in a Solid-State Device

P. Stoliar, O. Schneegans, and M. J. Rozenberg
Phys. Rev. Applied 16, 034030 – Published 16 September 2021
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Abstract

We study the minimal recurrent spiking neural network of a single neuron with an autaptic synapse. We implement the neural system in the solid state with a recently introduced ultracompact neuron (UCN) model, which is based on the memristive properties of a thyristor. The UCN is supplemented by a self-synaptic, autaptic, connection, where we control the feedback. Both excitatory and inhibitory cases are considered. We explore the systematic behavior as a function of autaptic intensity and feedback time delay. We realize a tunable dynamic memory, showing graded persistent activity, where short excitatory and inhibitory pulses allow the firing rate to be controlled. We finally reproduce recent experimentally observed behavior of a biological autapse measured in vivo, finding excellent qualitative agreement. Our work opens the way into the field of solid-state neuroscience, with the UCN as an accessible platform to implement and experimentally study the dynamic behavior of spiking neural networks.

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  • Received 15 December 2020
  • Revised 30 March 2021
  • Accepted 30 July 2021

DOI:https://doi.org/10.1103/PhysRevApplied.16.034030

© 2021 American Physical Society

Physics Subject Headings (PhySH)

Networks

Authors & Affiliations

P. Stoliar1,†, O. Schneegans2,†, and M. J. Rozenberg3,*,†

  • 1National Institute of Advanced Industrial Science and Technology (AIST), 305-8565 Tsukuba, Japan
  • 2CentraleSupélec, CNRS, Université Paris-Saclay, Sorbonne Université, Laboratoire de Génie Electrique et Electronique de Paris, 91192 Gif-sur-Yvette, France
  • 3Université Paris-Saclay, CNRS Laboratoire de Physique des Solides, 91405 Orsay, France

  • *marcelo.rozenberg@universite-paris-saclay.fr
  • P. Stoliar, O. Schneegans, and M. J. Rozenberg contributed equally to this work.

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Vol. 16, Iss. 3 — September 2021

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