Neuromorphic Electronic Model of Synaptic Plasticity

神经形态系统设计的最新进展已经使得可以通过在电流模式下操作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进行建模，所述互补金属氧化物半导体（CMOS）电路可以使用模拟超大规模集成电路（模拟VLSI）技术制造。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式神经假体设备。实际使用这种模拟VLSI神经形态系统来模拟大脑中的结构-功能关系的技术障碍是目前缺乏有效的手段来使这些硅神经系统在类似的CMOS仿真平台上学习和适应。哺乳动物大脑中的学习和记忆被广泛认为是由突触修饰引起的，例如兴奋性（谷氨酸能）或抑制性（GABA能）突触中的长时程增强（LTP）和抑制（LTD）。这个探索性/发展性（R21）项目试图使用CMOS模拟VLSI技术，通过逆向工程的基本细胞过程的基础上的各种形式的LTP和LTD兴奋/抑制性突触和它们在树突状网络的相互作用来模拟这些突触事件。由此产生的神经形态模拟VLSI模型的突触LTP和LTD将提供基本的系统构建模块，为未来继续发展的大规模神经网络模型，模仿学习和记忆功能的健康和精神疾病。

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.