Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey

• URL: http://arxiv.org/abs/2307.16235v1
• Date: Sun, 30 Jul 2023 13:57:25 GMT
• Title: Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey
• Authors: Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato
• Abstract summary: Bio-Inspired Deep Learning approaches towards advancing the computational capabilities and biological plausibility of current models. Recent bio-inspired training methods pose themselves as alternatives to backprop, both for traditional and spiking networks.
• Score: 9.284385189718236
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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