Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey
It provides a comprehensive overview for researchers interested in bridging neuroscience and AI, but it is incremental as it synthesizes existing work without presenting new results.
This survey reviews biologically-inspired AI approaches, focusing on Spiking Neural Networks (SNNs) and their training challenges, and discusses bio-inspired methods as alternatives to backpropagation for enhancing computational capabilities and biological plausibility.
For a long time, biology and neuroscience fields have been a great source of inspiration for computer scientists, towards the development of Artificial Intelligence (AI) technologies. This survey aims at providing a comprehensive review of recent biologically-inspired approaches for AI. After introducing the main principles of computation and synaptic plasticity in biological neurons, we provide a thorough presentation of Spiking Neural Network (SNN) models, and we highlight the main challenges related to SNN training, where traditional backprop-based optimization is not directly applicable. Therefore, we discuss recent bio-inspired training methods, which pose themselves as alternatives to backprop, both for traditional and spiking networks. Bio-Inspired Deep Learning (BIDL) approaches towards advancing the computational capabilities and biological plausibility of current models.