Effects of Memristive Synapse Radiation Interactions on Learning in Spiking Neural Networks

Sumedha Gandharava Dahl, Robert C. Ivans, Kurtis D. Cantley

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO 2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

Original languageAmerican English
Article number555
JournalSN Applied Sciences
Volume3
Issue number5
DOIs
StatePublished - Jul 2021

Keywords

  • Leaky integrate-and-fire (LIF) neuron
  • Neuromorphic circuits
  • Non-linear memristor model
  • Radiation
  • Spatio-temporal pattern learning
  • Spike-timing-dependent plasticity (STDP)

EGS Disciplines

  • Electrical and Computer Engineering

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