C. elegans chemotaxis inspired neuromorphic circuit for contour tracking and obstacle avoidance

Shibani Santurkar, Bipin Rajendran

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

5 Scopus citations

Abstract

We demonstrate a spiking neural network for navigation motivated by the chemotaxis circuit of Caenorhabditis elegans. Our network uses information regarding temporal gradients in intensity of local variables such as chemical concentration, temperature, radiation, etc., to make navigational decisions for contour tracking and obstacle avoidance. The gradient information is determined by mimicking the underlying mechanisms of the ASE neurons of C. elegans. Simulations show that our software-worm is able to identify the set-point with 92% efficiency, 68.5% higher than an optimal memoryless Lévy foraging strategy and 33% higher than an equivalent non-spiking neural network configuration. The software-worm is able to track the set-point with an average deviation of 1% from the set-point, and this performance degrades merely by 1.8% in the presence of intense salt and pepper noise in the local tracking variable. We also develop a VLSI implementation for the main gradient detector neurons, which could be integrated with standard comparator circuitry to develop robust circuits for navigation and contour tracking. We demonstrate noise-resilience of our network to environmental, architectural and circuit noise.

Original languageEnglish (US)
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2015-September
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

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

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