Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A
  Survey
Gabriele Lagani Fabrizio Falchi Claudio Gennaro Giuseppe Amato
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.