Mimicking the worm An adaptive spiking neural circuit for contour tracking inspired by C. Elegans thermotaxis

Ashish Bora, Arjun Rao, Bipin Rajendran

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

7 Scopus citations

Abstract

We demonstrate a spiking neural circuit with timing-dependent adaptive synapses to track contours in a two-dimensional plane. Our model is inspired by the architecture of the 7-neuron network believed to control the thermotaxis behavior in the nematode Caenorhabditis Elegans. However, unlike the C. Elegans network, our sensory neuron only uses the local variable (and not its derivative) to implement contour tracking, thereby minimizing the complexity of implementation. We employ spike timing based adaptation and plasticity rules to design micro-circuits for gradient detection and tracking. Simulations show that our bio-mimetic neural circuit can identify isotherms with a 60% higher probability than the theoretically optimal memoryless Levy foraging model. Further, once the set-point is identified, our model's tracking accuracy is in the range of ±0.05 °C, similar to that observed in nature. The neurons in our circuit spike at sparse biological rates ( 100 Hz), enabling energy-efficient implementations.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2079-2086
Number of pages8
ISBN (Electronic)9781479914845
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period7/6/147/11/14

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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