Innovation is in the Mind – The Converging Trajectories of IT, Neuro and Nano
Event details
| Date | 26.08.2013 |
| Hour | 17:15 |
| Speaker |
Prof. Jan Rabaey, University of California (Berkeley) Bio: He received the EE and Ph.D. degrees in Applied Sciences from the Katholieke Universiteit Leuven, Belgium, in 1978 and 1983 respectively. From 1983-1985, he was a Visiting Research Engineer at UC Berkeley. From 1985-1987, he was a research manager at IMEC, Belgium, and in 1987, joined the faculty of the Electrical Engineering and Computer Science department at UC Berkeley, where he is now holds the Donald O. Pederson Distinguished Professorship. He has been a visiting professor at the University of Pavia (Italy), Waseda University (Japan), the Technical University Delft (Netherlands), Victoria Technical University and the University of New South Wales (Australia). He was the Associate Chair (EE) of the EECS Dept. at Berkeley from 1999 until 2002 and is currently the Scientific co-director of the Berkeley Wireless Research Center (BWRC), as well as the director of the Multiscale Systems Research Center (MuSyC). Professor Rabaey has authored or co-authored a wide range of papers in the area of signal processing and design automation. He has received numerous scientific awards, including the 1985 IEEE Transactions on Computer Aided Design Best Paper Award (Circuits and Systems Society), the 1989 Presidential Young Investigator award, and the 1994 Signal Processing Society Senior Award. In 1995, he became an IEEE Fellow. He has also be awarded the 2002 ISSCC Jack Raper Award, the 2008 IEEE Circuits and Systems Mac Van Valkenburg Award, the 2009 EDAA Lifetime Achievement Award, and the 2010 Semiconductor Industry Association University Researcher Award. In 2011, he was elected to the Royal Flemish Academy of Arts and Sciences (Belgium). He is past Chair of the VLSI Signal Processing Technical Committee of the Signal Processing Society and has chaired the executive committee of the Design Automation Conference. He serves on the Technical Advisory Boards of a wide range of companies. |
| Location | |
| Category | Conferences - Seminars |
While major progress has been made over the past decades, the dynamic behavior of the brain at large is still ill understood. One of the major charters in the neuroscience community for the next decade is to create a dynamic map of brain activity – an initiative that most recently received the full support of the White House. Doing so will require the most advanced imaging capabilities operating at multiple scales of resolution – from 10’s of microns to the complete brain. Recent advances in microscopic sensing, ULP processing and communications are leading to brain-machine interfaces that may be able to observe thousands if not millions of active neurons in vivo. These “nanomorphic” circuits truly push the limit of nanometer scale semiconductor devices.
One of the unintended side effects of a deeper understanding of the operation of the brain is that in reverse it may lead to novel architectures and models for nanoscale information-processing systems. While the brain may not be considered “general purpose” in terms of its computational capabilities, it performs a set of functions such as feature extraction, classification, synthesis, recognition, learning, and higher-order decision-making amazingly well and efficiently. Some properties of neural processing match the features that are inherent to emerging nanoscale semiconductor fabrics: it thrives on randomness and variability, processing is performed in the continuous or discrete domains, and massive parallelism, major redundancy and adaptivity are of essence. Computational paradigms inspired by neural information processing hence may lead to energy-efficient, low-cost, dense and/or reliable implementations of the functions the brain excels at.
In this presentation, we will explore both sides of this neuroscience-information technology interaction. One thing is for sure – the joint future will be exciting.
One of the unintended side effects of a deeper understanding of the operation of the brain is that in reverse it may lead to novel architectures and models for nanoscale information-processing systems. While the brain may not be considered “general purpose” in terms of its computational capabilities, it performs a set of functions such as feature extraction, classification, synthesis, recognition, learning, and higher-order decision-making amazingly well and efficiently. Some properties of neural processing match the features that are inherent to emerging nanoscale semiconductor fabrics: it thrives on randomness and variability, processing is performed in the continuous or discrete domains, and massive parallelism, major redundancy and adaptivity are of essence. Computational paradigms inspired by neural information processing hence may lead to energy-efficient, low-cost, dense and/or reliable implementations of the functions the brain excels at.
In this presentation, we will explore both sides of this neuroscience-information technology interaction. One thing is for sure – the joint future will be exciting.
Practical information
- General public
- Free
Organizer
- Taveira Nicky <[email protected]>
Contact
- Taveira Nicky <[email protected]>