I propose to build Neurogrid, a specialized hardware platform that will perform cortex-scale emulations while offering? software-like flexibility. Recent breakthroughs in brain mapping present an unprecedented opportunity to understand? how the brain works, with profound implications for society. To understand the brain, we have to? interpret these richly growing observations by modeling the brain, the only way to test our understanding?? since building a real brain out of biological parts is currently infeasible. Neurogrid will emulate (simulate in? real-time) one million neurons connected by six billion synapses?making it possible to model vertical,? horizontal and top-down cortical interactions in biophysical detail.? My ability to bring this endeavor to fruition and my commitment to biomedicine is evident in my past accomplishments.? Over the past eight years, my lab has designed seven neuromorphic chips that model seven? neural systems?retina, cochlea, cochlear nucleus, thalamus, hippocampus, visual cortex, and retinotectal? development. To pursue such diverse projects, I established productive collaborations with six colleagues in? Penn?s Neuroscience Department; our work was a Scientific American cover story (May 2005). The visual cortex? chip illustrates the potential of Analog VLSI: Emulating 9,216 neurons, it is 2,765 times faster than the? state-of-the-art. However, neither this chip nor the other six is programmable.? Neurogrid will provide programmability by augmenting Analog VLSI with Digital VLSI, a mixed-mode approach? that combines the best of both worlds. While including biophysical detail in a model provides contact? with experiment, programmability supports replicating manipulations, performing controls, benchmarking? models, and exploring mechanism. Realizing these two critical functions without sacrificing scale will make? it possible to replicate tasks laboratory animals perform in biologically realistic models for the first time,? which I will do in close collaboration with two neurophysiologists (Matthew Dalva Ph.D. and William? Newsome Ph.D.).

Agency
National Institute of Health (NIH)
Institute
Office of The Director, National Institutes of Health (OD)
Type
NIH Director’s Pioneer Award (NDPA) (DP1)
Project #
5DP1OD000965-02
Application #
7292773
Study Section
Special Emphasis Panel (ZGM1-NDPA-G (P2))
Program Officer
Jones, Warren
Project Start
2006-09-28
Project End
2011-07-31
Budget Start
2007-08-01
Budget End
2008-07-31
Support Year
2
Fiscal Year
2007
Total Cost
$789,375
Indirect Cost
Name
Stanford University
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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Dethier, Julie; Gilja, Vikash; Nuyujukian, Paul et al. (2011) Spiking Neural Network Decoder for Brain-Machine Interfaces. Int IEEE EMBS Conf Neural Eng :
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