A Model for R(t) Elements and R(t)-Based Spike-Timing-Dependent Plasticity with Basic Circuit Examples

Roberts C. Ivans, Sumedha Gandhereva Dahl, Kurtis D. Cantley

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule observed in biology that leads to numerous behavioral and cognitive outcomes. Emulating STDP in electronic spiking neural networks with high-density memristive synapses is, therefore, of significant interest. While one popular method involves pulse-shaping the spiking neuron output voltages, an alternative approach is outlined in this article. The proposed STDP implementation uses time-varying dynamic resistance [ R(t) ] elements to achieve local synaptic learning from spike-pair STDP, spike triplet STDP, and firing rates. The R(t) elements are connected to each neuron circuit, thereby maintaining synaptic density and leveraging voltage division as a means of altering synaptic weight (memristor voltage). Example R(t) elements with their corresponding behaviors are demonstrated through simulation. A three-input-two-output network using single-memristor synaptic connections and R(t) elements is also simulated. Network-level effects, such as nonspecific synaptic plasticity, are discussed. Finally, spatiotemporal pattern recognition (STPR) using R(t) elements is demonstrated in simulation.

Original languageAmerican English
Article number8935410
Pages (from-to)4206-4216
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number10
DOIs
StatePublished - Oct 2020

Keywords

  • Memristor
  • spike-timing-dependent plasticity (STDP)
  • spiking neural network
  • synapse

EGS Disciplines

  • Electrical and Computer Engineering

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