Implementation of Associative Memory with Online Learning into a Spiking Neural Network on Neuromorphic Hardware

Abstract

Implementing cognitive algorithms on robots is one potential direction to realize autonomous artificial agents. There is an effort to push robotics and artificial intelligence into many aspects of daily life. An important step in this process is leveraging concepts known to work from human cognitive features on computer systems to improve the performance of robotic systems. Spiking Neural Networks (SNNs) allow these computational models to be instantiated in a low size, weight, and power (SWaP) form factor due to the biological efficiencies they approximate.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2020
Accession Number
AD1108514

Entities

People

  • Michael J Hampo

Organizations

  • University of Dayton

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Autonomous Systems
  • Computers
  • Content Addressable Memory
  • Distance Learning
  • Efficiency
  • Energy Consumption
  • Energy Efficiency
  • Engineering
  • Information Science
  • Machine Learning
  • Neural Networks
  • Simulators

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks
  • Autonomy