Neuromorphic Electronic Model of Synaptic Plasticity

描述(由申请人提供)：神经形态系统设计的最新进展使得通过工作在电流模式下的互补金属氧化物半导体(CMOS)电路来对尖峰神经元或神经网络建模成为可能，该电路可以使用模拟超大规模集成电路(模拟VLSI)技术来制造。这种使能技术催生了各种新兴的生物医学应用，包括神经元网络的高速模拟、用于神经元-计算机接口的实时动态电流钳和脑植入神经假体设备。这种模拟VLSI神经形态系统用于模拟大脑中的结构-功能关系的实际使用的一个技术障碍是，目前缺乏让这些硅神经系统在类似的CMOS模拟平台上学习和适应的有效手段。哺乳动物大脑中的学习和记忆被广泛认为是兴奋性(谷氨酸能)或抑制性(GABA能)突触的长时程增强(LTP)和抑制(LTD)等突触修饰的结果。这个探索性/发展性(R21)项目试图通过反向工程各种形式的LTP和LTD在兴奋性/抑制性突触中的基本细胞过程以及它们在树突网络中的相互作用，使用CMOS模拟VLSI技术来模拟这些突触事件。由此产生的突触LTP和LTD的神经形态模拟VLSI模型将为未来继续开发模拟健康和精神疾病中学习和记忆功能的大规模神经元网络模型提供基本的系统构建块。

DESCRIPTION (provided by applicant): Recent advances in neuromorphic systems design have made it possible to model spiking neurons or neural networks by means of complementary metal oxide semiconductor (CMOS) circuits operating in current mode, which can be fabricated using analog very-large-scale-integrated circuit (analog VLSI) technology. This enabling technology has spawned a variety of emerging biomedical applications including high-speed simulation of neuronal networks, real-time dynamic current-clamp for neuron-computer interface and brain-implantable neural prosthetic devices. A technological hurdle for the practical use of such analog VLSI neuromorphic systems to emulate the structure-function relationships in the brain is the current lack of an effective means for these silicon neural systems to learn and to adapt on similar CMOS simulation platforms. Learning and memory in the mammalian brain are widely assumed to result from synaptic modifications such as long-term potentiation (LTP) and depression (LTD) in excitatory (glutamatergic) or inhibitory (GABAergic) synapses. This exploratory/developmental (R21) project attempts to model these synaptic events using CMOS analog VLSI technology by reverse engineering the basic cellular processes underlying various forms of LTP and LTD in excitatory/inhibitory synapses and their interactions in dendritic networks. The resulting neuromorphic analog VLSI models of synaptic LTP and LTD will provide the basic system building blocks for continuing future development of large-scale neuronal network models that emulate learning and memory functions in health and in mental disease.