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

Indeed, biological brains exhibit extraordinary capabilities in terms of energy efficiency, supporting advanced cognitive functions while consuming only 20W [89]. It is believed that the key to the energy efficient computation of biological neurons lies in the particular coding paradigm based on short pulses, or spikes [61]. SNN models aim at simulating the behavior of biological neurons more realistically, compared to traditional DNNs. As a result, SNNs are well suited for energy-efficient implementations in neuromorphic [84, 174, 186, 190, 229] or biological [92, 111, 176] hardware. This makes SNNs a promising direction toward energy-efficient DL. Unfortunately, training SNNs is not trivial, as traditional optimization based on the backpropagation algorithm (backprop) is not directly applicable [165]. In fact, the biological plausibility of backprop - the workhorse of DL - is questioned by neuroscientists [73, 113, 130, 157, 172]. Therefore, researchers took again inspiration from biology, in order to find new learning solutions as alternatives to backprop. The goal was not only to address the problem of SNN training [33, 148], but also to discover novel approaches to the learning problem [77, 139, 182], and possibly more data efficient strategies [69, 90, 105-107, 110].