Simplified Klinokinesis using Spiking Neural Networks for Resource-Constrained Navigation on the Neuromorphic Processor Loihi

TL;DR

This paper presents a simplified, energy-efficient neuromorphic implementation of chemotaxis using klinokinesis on Intel's Loihi processor, achieving performance comparable to software models and robustness in noisy conditions.

Contribution

It demonstrates a novel adaptation of klinokinesis-based chemotaxis to Loihi using only LIF neurons and spike-based functions, simplifying biomimetic navigation on neuromorphic hardware.

Findings

Loihi implementation matches Python software performance in foraging and contour tracking

The neuromorphic chemotaxis is resilient to noisy environments

The approach enables complex robotic control using SNN blocks on Loihi

Abstract

C. elegans shows chemotaxis using klinokinesis where the worm senses the concentration based on a single concentration sensor to compute the concentration gradient to perform foraging through gradient ascent/descent towards the target concentration followed by contour tracking. The biomimetic implementation requires complex neurons with multiple ion channel dynamics as well as interneurons for control. While this is a key capability of autonomous robots, its implementation on energy-efficient neuromorphic hardware like Intel's Loihi requires adaptation of the network to hardware-specific constraints, which has not been achieved. In this paper, we demonstrate the adaptation of chemotaxis based on klinokinesis to Loihi by implementing necessary neuronal dynamics with only LIF neurons as well as a complete spike-based implementation of all functions e.g. Heaviside function and subtractions. Our results show that Loihi implementation is equivalent to the software counterpart on Python in terms of performance - both during foraging and contour tracking. The Loihi results are also resilient in noisy environments. Thus, we demonstrate a successful adaptation of chemotaxis on Loihi - which can now be combined with the rich array of SNN blocks for SNN based complex robotic control.