Neuromorphic Electronic Model of Synaptic Plasticity

突触可塑性的神经形态电子模型

基本信息

批准号：
7084439
负责人：
CHI-SANG POON
金额：
$ 23.73万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2005
资助国家：
美国
起止时间：
2005-07-01 至 2009-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7084439
关键词：
NMDA receptors computational neuroscience hippocampus long term potentiation metal oxide model design /development neural plasticity neural transmission semiconduction synapses

项目摘要

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.

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在电流模式下运行的互补金属氧化物半导体（CMOS）电路对尖峰神经元或神经网络进行建模成为可能，该电路可以使用模拟超大型晶体管来制造。规模集成电路（模拟VLSI）技术。这种使能技术催生了各种新兴的生物医学应用，包括神经网络的高速模拟、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。实际使用此类模拟 VLSI 神经形态系统来模拟大脑中的结构功能关系的一个技术障碍是，目前缺乏有效的手段让这些硅神经系统在类似的 CMOS 模拟平台上学习和适应。人们普遍认为哺乳动物大脑中的学习和记忆是由突触修饰引起的，例如兴奋性（谷氨酸能）或抑制性（GABA能）突触的长时程增强（LTP）和抑制（LTD）。该探索性/开发性 (R21) 项目试图通过对兴奋性/抑制性突触中各种形式的 LTP 和 LTD 及其在树突网络中的相互作用的基本细胞过程进行逆向工程，使用 CMOS 模拟 VLSI 技术对这些突触事件进行建模。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的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。