A neural circuit for navigation inspired by C. elegans Chemotaxis

TL;DR

This paper presents a robust, noise-resilient spiking neural network inspired by C. elegans chemotaxis for contour tracking and navigation, outperforming traditional models in accuracy and efficiency.

Contribution

The authors develop a novel 7-spiking neuron neural circuit modeled after C. elegans that enhances robustness and efficiency in navigation tasks compared to non-spiking networks.

Findings

Achieves four times higher probability of set-point detection than Levy foraging model.

Demonstrates superior noise resilience and efficiency over non-spiking networks.

Can be adapted for obstacle avoidance and extended to 3D contour tracking.

Abstract

We develop an artificial neural circuit for contour tracking and navigation inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to harness the computational advantages spiking neural networks promise over their non-spiking counterparts, we develop a network comprising 7-spiking neurons with non-plastic synapses which we show is extremely robust in tracking a range of concentrations. Our worm uses information regarding local temporal gradients in sodium chloride concentration to decide the instantaneous path for foraging, exploration and tracking. A key neuron pair in the C. elegans chemotaxis network is the ASEL & ASER neuron pair, which capture the gradient of concentration sensed by the worm in their graded membrane potentials. The primary sensory neurons for our network are a pair of artificial spiking neurons that function as gradient detectors whose design is adapted from a computational model of the ASE neuron pair in C. elegans. Simulations show that our worm is able to detect the set-point with approximately four times higher probability than the optimal memoryless Levy foraging model. We also show that our spiking neural network is much more efficient and noise-resilient while navigating and tracking a contour, as compared to an equivalent non-spiking network. We demonstrate that our model is extremely robust to noise and with slight modifications can be used for other practical applications such as obstacle avoidance. Our network model could also be extended for use in three-dimensional contour tracking or obstacle avoidance.