NIH Director's Pioneer Award

Stanford University, Stanford CA

Investigators

Linked publications & trials

Abstract

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