Spiking Neural Networks - Part I: Detecting Spatial Patterns

Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Spiking Neural Networks (SNNs) are biologically inspired machine learning models that build on dynamic neuronal models processing binary and sparse spiking signals in an event-driven, online, fashion. SNNs can be implemented on neuromorphic computing platforms that are emerging as energy-efficient co-processors for learning and inference. This is the first of a series of three letters that introduce SNNs to an audience of engineers by focusing on models, algorithms, and applications. In this first letter, we first cover neural models used for conventional Artificial Neural Networks (ANNs) and SNNs. Then, we review learning algorithms and applications for SNNs that aim at mimicking the functionality of ANNs by detecting or generating spatial patterns in rate-encoded spiking signals. We specifically discuss ANN-to-SNN conversion and neural sampling. Finally, we validate the capabilities of SNNs for detecting and generating spatial patterns through experiments.

Original languageEnglish (US)
Article number9317739
Pages (from-to)1736-1740
Number of pages5
JournalIEEE Communications Letters
Volume25
Issue number6
DOIs
StatePublished - Jun 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Neuromorphic computing
  • spiking neural networks

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