Cognitive Interaction Technology
Event details
| Date | 19.02.2013 |
| Hour | 14:30 |
| Speaker | Elisabetta Chicca, Center of Excellence, Bielefeld University, Germany Bio : Elisabetta Chicca studied physics at the University of Rome "La Sapienza" Italy, where she graduated in 1999. In 2006 she received a PhD in Natural Sciences from the Physics department of the Federal Institute of Technology Zurich (ETHZ), Switzerland, and a PhD in Neuroscience from the Neuroscience Center Zurich (ZNZ). Immediately after the PhD, she started a PostDoc at the Institute of Neuroinformatics at the University of Zurich and ETH Zurich, where she continued working as Research Group Leader from May 2010 to August 2011. Since August 2011, she is an assistant professor at Bielefeld University and is heading the Neuromorphic Circuits Group based in the Faculty of Technology. |
| Location |
INF 328
|
| Category | Conferences - Seminars |
Centre SI SEMINAR
The styles of computation used by nervous systems are fundamentally different from those used by conventional computers: biological neural networks process information using energy-efficient asynchronous, event-driven, methods. They are adaptive, fault-tolerant, self-repairing, learn from their interactions with the environment, and can flexibly compose complex behaviors by combining multiple instances of simpler elements. These biological abilities offer an attractive alternative to conventional computing strategies. Neuromorphic systems are composed of VLSI devices with hybrid analog/digital circuits that implement hardware models of biological nervous systems. Neuromorphic systems share to a large extent the same physical constraints of their biological counterparts. Therefore they often have to use similar strategies for maximizing compactness, optimizing robustness to noise, minimizing power consumption, and increasing fault tolerance.
In this talk, I will describe basic features of the the circuital building blocks used in the design of multi-neuron neuromorphic chips, their use for building multi-chip systems implementing spiking neural networks, and their application to modelling biological nervous systems.
The styles of computation used by nervous systems are fundamentally different from those used by conventional computers: biological neural networks process information using energy-efficient asynchronous, event-driven, methods. They are adaptive, fault-tolerant, self-repairing, learn from their interactions with the environment, and can flexibly compose complex behaviors by combining multiple instances of simpler elements. These biological abilities offer an attractive alternative to conventional computing strategies. Neuromorphic systems are composed of VLSI devices with hybrid analog/digital circuits that implement hardware models of biological nervous systems. Neuromorphic systems share to a large extent the same physical constraints of their biological counterparts. Therefore they often have to use similar strategies for maximizing compactness, optimizing robustness to noise, minimizing power consumption, and increasing fault tolerance.
In this talk, I will describe basic features of the the circuital building blocks used in the design of multi-neuron neuromorphic chips, their use for building multi-chip systems implementing spiking neural networks, and their application to modelling biological nervous systems.
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