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

Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato
7/30/2023
cs.NEcs.AIcs.CV

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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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Cite this paper

@article{lagani2023spiking,
  title  = {Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey},
  author = {Gabriele Lagani and Fabrizio Falchi and Claudio Gennaro and Giuseppe Amato},
  year   = {2023},
  eprint = {2307.16235},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.16235v1}
}

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