Photonic Implementation of Spike-Timing-Dependent Plasticity and Learning Algorithms of Biological Neural Systems

Ryan Toole, Alexander N. Tait, Thomas Ferreira De Lima, Mitchell A. Nahmias, Bhavin J. Shastri, Paul R. Prucnal, Mable P. Fok

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

The neurobiological learning algorithm, spike-timing-dependent plasticity (STDP), is demonstrated in a simple photonic system using the cooperative nonlinear effects of cross gain modulation and nonlinear polarization rotation, and supervised and unsupervised learning using photonic neuron principles are examined. An STDP-based supervised learning scheme is presented which is capable of mimicking a desirable spike pattern through learning and adaptation. Furthermore, unsupervised learning is illustrated by a principal component analysis system operating under similar learning rules. Finally, a photonic-distributed processing network capable of STDP-based unsupervised learning is theoretically explored.

Original languageEnglish (US)
Article number7234839
Pages (from-to)470-476
Number of pages7
JournalJournal of Lightwave Technology
Volume34
Issue number2
DOIs
StatePublished - Jan 15 2016

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics

Keywords

  • Feedback circuits
  • information theory
  • neural networks
  • nonlinear optics
  • optical signal processing

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