NIH Director's Pioneer Award

我建议建立神经网格，一个专门的硬件平台，将执行皮层规模的仿真，同时提供 软件般的灵活性。最近在脑图谱方面的突破为理解 大脑是如何工作的，对社会有着深远的影响。为了了解大脑，我们必须 通过模拟大脑来解释这些丰富的观察结果，这是测试我们理解力的唯一方法？ 因为用生物器官构建真实的大脑目前是不可行的。Neurogrid将模拟（在 实时）一百万个神经元由六十亿个突触连接？使得垂直建模成为可能， 水平和自上而下的生物物理细节的皮质相互作用。 我的能力，使这一奋进取得成果，我的承诺，生物医学是显而易见的，在我过去的成就。 在过去的八年里，我的实验室设计了七个神经形态芯片， 神经系统？视网膜、耳蜗、耳蜗核、丘脑、海马、视皮层和视网膜顶盖 发展为了追求如此多样化的项目，我与六位同事建立了富有成效的合作关系， 佩恩？我们的工作是科学美国人的封面故事（2005年5月）。视觉皮层 芯片展示了模拟超大规模集成电路的潜力：模拟9，216个神经元，比模拟器快2，765倍。 然而，无论是这个芯片还是其他六个芯片都是可编程的。 神经网格将通过用数字VLSI增强模拟VLSI来提供可编程性，这是一种混合模式方法 结合了两个世界的精华虽然在模型中包括生物物理细节提供了联系 通过实验，可编程性支持重复操作、执行控制、基准测试 模型和探索机制。在不牺牲规模的情况下实现这两个关键功能， 第一次有可能复制实验室动物在生物现实模型中执行的任务， 我将与两位神经生理学家（Matthew Dalva博士）密切合作，和威廉 Newsome Ph.D.）。

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.).