Adaptive Chemotaxis for improved Contour Tracking using Spiking Neural Networks

TL;DR

This paper introduces an adaptive spiking neural network model inspired by worm chemotaxis, combining klinokinesis, klinotaxis, and orthokinesis to improve autonomous contour tracking efficiency and accuracy.

Contribution

It presents a novel integrated model of chemotaxis mechanisms in SNNs, enabling faster and more precise contour tracking than previous methods.

Findings

2.4x reduction in time to reach setpoint

8.7x reduction in average deviation from setpoint

Successful integration of klinokinesis, klinotaxis, and orthokinesis

Abstract

In this paper we present a Spiking Neural Network (SNN) for autonomous navigation, inspired by the chemotaxis network of the worm Caenorhabditis elegans. In particular, we focus on the problem of contour tracking, wherein the bot must reach and subsequently follow a desired concentration setpoint. Past schemes that used only klinokinesis can follow the contour efficiently but take excessive time to reach the setpoint. We address this shortcoming by proposing a novel adaptive klinotaxis mechanism that builds upon a previously proposed gradient climbing circuit. We demonstrate how our klinotaxis circuit can autonomously be configured to perform gradient ascent, gradient descent and subsequently be disabled to seamlessly integrate with the aforementioned klinokinesis circuit. We also incorporate speed regulation (orthokinesis) to further improve contour tracking performance. Thus for the first time, we present a model that successfully integrates klinokinesis, klinotaxis and orthokinesis. We demonstrate via contour tracking simulations that our proposed scheme achieves an 2.4x reduction in the time to reach the setpoint, along with a simultaneous 8.7x reduction in average deviation from the setpoint.