Topic: neuromorphic circuits

The success of Artificial Neural Networks (ANNs) trained with error-backpropagation algorithms relies on the combination of offline training, vast amounts of data, and energy-intensive simulations on dedicated hardware accelerators. In contrast, biological neurons can learn quickly in a self-supervised manner by utilizing (a) recurrent connections within their local network and from other areas, (b) segregated dendrites receiving inputs from feedforward and feedback pathways, and (c) learning rules that combine locally available information from pre- and -post-synaptic sources. Inspired by loc