Sub-threshold CMOS Spiking Neuron Circuit Design for Navigation Inspired by C. elegans Chemotaxis
This work addresses navigation challenges for robotics or low-power systems, but it is incremental as it adapts known biological mechanisms to circuit design.
The paper tackled the problem of designing a spiking neural network for navigation inspired by C. elegans chemotaxis, resulting in a VLSI implementation for gradient detector neurons that enables foraging and target tracking in noisy environments.
We demonstrate a spiking neural network for navigation motivated by the chemotaxis network of Caenorhabditis elegans. Our network uses information regarding temporal gradients in the tracking variable's concentration to make navigational decisions. The gradient information is determined by mimicking the underlying mechanisms of the ASE neurons of C. elegans. Simulations show that our model is able to forage and track a target set-point in extremely noisy environments. We develop a VLSI implementation for the main gradient detector neurons, which could be integrated with standard comparator circuitry to develop a robust circuit for navigation and contour tracking.