Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning:
  A Survey
Gabriele Lagani Fabrizio Falchi Claudio Gennaro Giuseppe Amato
Recently emerged technologies based on Deep Learning (DL) achieved
outstanding results on a variety of tasks in the field of Artificial
Intelligence (AI). However, these encounter several challenges related to
robustness to adversarial inputs, ecological impact, and the necessity of huge
amounts of training data. In response, researchers are focusing more and more
interest on biologically grounded mechanisms, which are appealing due to the
impressive capabilities exhibited by biological brains. This survey explores a
range of these biologically inspired models of synaptic plasticity, their
application in DL scenarios, and the connections with models of plasticity in
Spiking Neural Networks (SNNs). Overall, Bio-Inspired Deep Learning (BIDL)
represents an exciting research direction, aiming at advancing not only our
current technologies but also our understanding of intelligence.