Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats

Abstract

We demonstrate a spiking neural circuit for azimuth angle detection inspired by the echolocation circuits of the Horseshoe bat Rhinolophus ferrumequinum and utilize it to devise a model for navigation and target tracking, capturing several key aspects of information transmission in biology. Our network, using only a simple local-information based sensor implementing the cardioid angular gain function, operates at biological spike rate of 10 Hz. The network tracks large angular targets (60 degrees) within 1 sec with a 10% RMS error. We study the navigational ability of our model for foraging and target localization tasks in a forest of obstacles and show that our network requires less than 200X spike-triggered decisions, while suffering only a 1% loss in performance compared to a proportional-integral-derivative controller, in the presence of 50% additive noise. Superior performance can be obtained at a higher average spike rate of 100 Hz and 1000 Hz, but even the accelerated networks requires 20X and 10X lesser decisions respectively, demonstrating the superior computational efficiency of bio-inspired information processing systems.