VS2N : Interactive Dynamic Visualization and Analysis Tool for Spiking Neural Networks

Bio-inspired computing architectures enable ultra-low power consumption and massive parallelism using neuromorphic computing, which is apt to implement Spiking Neural Networks (SNN). Such architectures are particularly suitable for energy-constrained applications. A deeper understanding of Spiking Neural Networks (SNN) behavior during training is needed to improve these architectures. This paper presents VS2N, a web-based tool for interactive visualization and analysis of SNN activity over time. This simulator-independent tool offers a way to examine, analyze and validate different hypotheses about SNN activity. We present available analysis modules and use-cases of the tool as an example.

I. Introduction

Bio-inspired technology has attracted attention lately due to the advantages that such technology offers, in particular the massive parallelism and low power consumption, which makes it suitable for energy-constrained applications, especially natural data processing applications. This technology provides neuromorphic computing by using Spiking Neural Networks (SNNs) and it is considered as one of the most promising alternatives to the Von Neumann architecture for "more-than-Moore" computing.