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SUMMARY:Cognitive Interaction Technology
DTSTART:20130219T143000
DTSTAMP:20260407T043719Z
UID:8495bfb84c8c227bcb15a335459fa137ba7d6350c9d97d0a3a7e5a67
CATEGORIES:Conferences - Seminars
DESCRIPTION:Elisabetta Chicca\, Center of Excellence\, Bielefeld  Univers
 ity\, Germany Bio : Elisabetta Chicca studied physics at the University of
  Rome "La  Sapienza" Italy\, where she graduated in 1999. In 2006 she rec
 eived a  PhD in Natural Sciences from the Physics department of the Feder
 al  Institute of Technology Zurich (ETHZ)\, Switzerland\, and a PhD in  
 Neuroscience from the Neuroscience Center Zurich (ZNZ). Immediately  afte
 r the PhD\, she started a PostDoc at the Institute of  Neuroinformatics a
 t 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.\nCen
 tre SI SEMINAR\nThe styles of computation used by nervous systems are fund
 amentally 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-repairin
 g\, learn from their interactions with the environment\, and can flexibly 
 compose complex behaviors by combining multiple instances of simpler eleme
 nts. These biological abilities offer an attractive alternative to convent
 ional computing strategies.  Neuromorphic systems are composed of VLSI de
 vices with hybrid analog/digital circuits that implement hardware models o
 f 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\, op
 timizing robustness to noise\, minimizing power consumption\, and increasi
 ng fault tolerance.\nIn this talk\, I will describe basic features of the 
 the circuital building blocks used in the design of multi-neuron neuromorp
 hic chips\, their use for building multi-chip systems implementing spiking
  neural networks\, and their application to modelling biological nervous s
 ystems.
LOCATION:INF 328
STATUS:CONFIRMED
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