Arithmetic computing via rate coding in neural circuits with spike-triggered adaptive synapses

Sushrut Thorat, Bipin Rajendran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present spiking neural circuits with spike-time dependent adaptive synapses capable of performing a variety of basic mathematical computations. These circuits encode and process information in the spike rates that lie between 40-140 Hz. The synapses in our circuit obey simple, local and spike-time dependent adaptation rules. We demonstrate that our circuits can perform the fundamental operations - addition, subtraction, multiplication and division, as well as other non-linear transformations such as exponentiation and logarithm for time dependent signals in real-time. We show that our spiking neural circuits are tolerant to a high degree of noise in the input variables, and illustrate its computational capability in an exemplary signal estimation problem. Our circuits can thus be used in a wide variety of hardware and software implementations for navigation, control and computation.

Original languageEnglish (US)
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs
StatePublished - Sep 28 2015
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: Jul 12 2015Jul 17 2015

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2015-September

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2015
CountryIreland
CityKillarney
Period7/12/157/17/15

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Keywords

  • Estimation
  • MATLAB
  • Neurons

Fingerprint Dive into the research topics of 'Arithmetic computing via rate coding in neural circuits with spike-triggered adaptive synapses'. Together they form a unique fingerprint.

Cite this