Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats
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 approximately 10 Hz. The network tracks large angular targets (60
• ) 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 it requires less than 200X spike-triggered decisions, while suffering less than 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 require 20X and 10X lesser decisions respectively, demonstrating the superior computational efficiency of bio-inspired information processing systems.
The authors design a biologically inspired neural circuit for controlling the angle of a bat in relation to targets and obstacles detected with echolocation. The proposed neural circuit is modelled as a spiking neural network (SNN) and estimates the angle to objects. Experimental evaluation looks at spike rate in the presence of environment noise and show that a higher spike rate is not necessarily best when the environment is very noisy. The method is also compared to a PID controller on a foraging task and performs better with less computation.
I found this paper difficult to judge because I'm unsure of what the intended contribution is. From my reading of it, the authors propose a SNN model that is based on available literature of how bats track target angles. Design decisions throughout the paper are chosen to make the model more bat like. The intent is that by designing this system the authors are able to show that biologically based systems can outperform a classic control algorithm while being more computationally efficient.   In general, the technical quality of the paper is strong with several experiments, thorough analysis of them, and the method described clearly. The authors first look at a parameter of the model (spike rate) and show that a high spike rate is usually good it can be problematic when environment noise is high. They next compare their model to a PID controller for a foraging and target avoidance task. One concern I have about this experiment is that the PID controller is tuned to match characteristics of the proposed model. If the comparison is being made on % success, then the PID controller should be tuned to maximize success.   To the best of my knowledge, this is the first work that has modelled azimuth angle detection with a Spiking NN. The proposed model outperforms a PID controller for a foraging task which shows that, in this instance, biological systems can outperform a classic control algorithm.   My biggest concern with the paper is the generality of its contribution. While it is interesting that a biologically inspired network can out perform a PID controller, I'm not sure what the main contribution is aside from azimuth angle detection that mimics what is known about how bats solve this problem. If nothing more can be said about how this could relate to modelling other tasks in other animals or applying SNNs to other problems then the impact seems very low.  The paper is well written. In most places it is very clear except for some details. I'm not sure why 16 clone networks are used. The paragraph describing this (100 - 105) is vague and I can only assume it is a form of averaging although I'm not sure why this is needed. I'm also unsure about how the network parameters were chosen. There is a statement that they were chosen so the network could work with different spike rates (Line 111) but wouldn't it make more sense to find the parameters that worked best for each spike rate and use those?  Minor comments: In general the paper uses a lot of acronyms which are hard to keep in memory while reading. If the authors think this is unavoidable then it would be useful to periodically redefine them. This would enhance the readability of the paper.   [Update: I read the author's response and am satisfied with answer on how PID was tuned. After reading other reviews and author's response I raised my novelty score.]