Giacomo Indiveri

Giacomo Indiveri
University of Zurich/ETH Zurich
Zurich, Switzerland

Speaker of Workshop 3

Will talk about: Learning and plasticity in neuromorphic systems

Bio sketch:

Giacomo Indiveri is a Professor at the Faculty of Science of the University of Zurich, Switzerland. He obtained an M.Sc. degree in electrical engineering and a Ph.D. degree in computer science from the University of Genoa, Italy. Indiveri was a post-doctoral research fellow in the Division of Biology at Caltech and at the Institute of Neuroinformatics of the University of Zurich and ETH Zurich. In 2006 he attained the "habilitation" in Neuromorphic Engineering at the ETH Zurich Department of Information Technology and Electrical Engineering, and in 2011 he won an ERC Starting Grant on "Neuromorphic processors: event-based VLSI models of cortical circuits for brain-inspired computation".  His research interests lie in the study of neural computation, with particular interest in spike-based learning and selective attention mechanisms, and in the hardware implementation of real-time sensory-motor systems using neuromorphic circuits and VLSI technology.

 

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.