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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