Learning and plasticity in neuromorphic systems

Talk abstract: For many practical tasks that involve real-time interactions with the environment, conventional computing systems cannot match the performance of biological ones. One of the reasons is that the architecture of nervous systems is very different from that of today's computers. Recently developed brain-inspired hardware architectures that emulate the biophysics of neurons and synapses in silicon represent a promising technology for implementing alternative low-power and compact computing paradigms.

In this presentation, I will present an overview of past and present neurocomputing approaches and propose hybrid analog/digital circuits that directly emulate the properties of neurons and synapses. I will show how they can be configured to implement real-time compact neural processing systems, describe hardware models of  spiking neurons, synaptic dynamics, and synaptic plasticity mechanisms, and propose methods for synthesizing real-time neuromorphic cognitive systems.