Impact of Radiation on Pattern Recognition in Memristor-Based Neuromorphic Circuits

Abstract

By the nature of their dense interconnectivity, future electronic spiking neural networks (SNNs) have the potential to be extraordinarily robust and defect-tolerant, in addition to energy-efficient. In this research, the effects on SNNs of high levels of radiation generated by weapons of mass destruction is explored through advanced modeling and simulation. Effects of this radiation on the spike timing-dependent plasticity learning rule, system stability, and pattern learning ability of the spiking neural network are analyzed.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2021
Accession Number
AD1137552

Entities

People

  • Kurtis D. Cantley

Organizations

  • Boise State University

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Alzheimer Disease
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Brain
  • Computational Science
  • Computer Vision
  • Data Analysis
  • Data Mining
  • Diseases And Disorders
  • Electronics
  • Environment
  • Failure Mode And Effect Analysis
  • Gamma Rays
  • Information Science
  • Integrated Circuits
  • Ionizing Radiation
  • Memristors
  • Neural Networks
  • Neurodegeneration
  • Pattern Recognition
  • Radiation Effects
  • Semiconductors

Readers

  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.
  • Pulsed Power and Plasma Physics.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Microelectronics