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 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.).