Elisabetta Chicca on spike-based learning

Spike...Spike.....Spike..Spike........Spike.  That's how the brain communicates. But how do you spikes help you learn? And more importantly, how do you modify a spiking network to learn new things (like speech and music)? The colloquial rule is "those who fire together, wire together" but in practice you need causality. A more realistic learning model is called spike-timing-dependent plasticity, where a particular neural connection is strengthened when an input spike immediately precedes the output spike. An input spike must happen before the output.  This is the backprop, for those of you from the machine-learning or DNN world, of the real neural world.

Elisabetta Chicca is a physicist and neuroscientist who implements her neural learning models in silicon. She builds chips that allow her to test her hypothesis in larger systems than could be done in silicon. As we build models of higher functions in the brain, these large-scale simulations are important to validate the low-level theories.

Who: Elisabetta Chicca (Bielefeld University)
What: Learning /in silico/ beyond Spike Timing Dependent Plasticity (STDP)
When: Thursday December 7 at 5PM <<< Note special time!!!
Where: CCRMA Seminar Room (Top Floor of the Knoll at Stanford
Why: Because all our neurons want to learn.



Learning /in silico/ beyond Spike Timing Dependent Plasticity (STDP)
Elisabetta Chicca - Bielefeld University

Synaptic plasticity empowers biological nervous systems with the ability to learn from experience and adjust to environmental changes. Such abilities are a must for artificial autonomous systems and therefore researchers have been devoting significant efforts to the understanding and modelling of plasticity mechanisms. In particular, the field of neuromorphic engineering focuses on the development of full-custom hybrid analog/digital electronic systems for the implementation of models of biological computation and learning in hardware. I will give a short historical overview of the most important plasticity circuits developed following the approach originally proposed by Carver Mead in the late eighties. Afterwards, I will present recent advancements in this field.


Elisabetta Chicca obtained a "Laurea" degree (M.Sc.) in Physics from the University of Rome 1 "La Sapienza", Italy in 1999, a Ph.D. in Natural Science from the Swiss Federal Institute of Technology Zurich (ETHZ, Physics department) and a Ph.D. in Neuroscience from the Neuroscience Center Zurich, in 2006. E. Chicca has carried out her research as a Postdoctoral fellow (2006-2010) and as a Group Leader (2010-2011) at the Institute of Neuroinformatics (University of Zurich and ETH Zurich) working on development of neuromorphic signal processing and sensory systems. Since 2011 she has lead the Neuromorphic Behaving Systems research group at Bielefeld University (Faculty of Technology and Cognitive Interaction Technology Center of Excellence, CITEC). Her current interests are in the development of VLSI models of cortical circuits for brain-inspired computation, learning in spiking VLSI neural networks and memristor based systems, bio-inspired sensing (olfaction, active electrolocation, audition) and motor control.