GenericSNN: A Framework for Easy Development of Spiking Neural Networks

Abstract

Spiking Neural Networks (SNNs) have emerged as a prominent paradigm for brain-inspired computing, capable of processing temporal information and event-driven data in an efficient and biologically plausible manner. However, their revolutionary and complex nature is one of the key reasons why SNNs are not yet a widely used approach in contrast to traditional Artificial Neural Networks (ANNs). In this paper, we present a comprehensive SNN framework that offers user-friendly implementation. It has been designed so that it is compatible with other well-known software tools for data science, being easy to integrate with them. We showcase the versatility of the framework by applying it to various well-known benchmarking datasets, including image processing of handwritten numbers, time-series forecasting and an advance use case for speech recognition, achieving competitive results compared to traditional ANNs. Our SNN framework aims to bridge the gap between neuroscience and artificial intelligence, empowering researchers and practitioners with an accessible tool to explore the potential of neuro-inspired computing in advancing the field of AI.