Mesoscale neural plasticity helps in AI learning

A joint research team led by Xu Bo from the Institute of Automation and Mu-Ming Poo from the Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, have discovered that self-backpropagation, a form of mesoscale synaptic plasticity rule in natural neural networks, can elevate the accuracy and reduce the computational cost of spiking neural networks (SNNs) and artificial neural networks (ANNs).

This article was originally published on this website.