Collaborative Research: A Neurodynamic Programming Approach for the Modeling, Analysis, and Control of Nanoscale Neuromorphic Systems

协作研究：用于纳米级神经形态系统建模、分析和控制的神经动力学编程方法

• 批准号: 1227877
• 负责人: Silvia Ferrari
• 金额: $ 24万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1227877&HistoricalAwards=false
• 关键词: Collaborative; Research; Neurodynamic; Programming; Approach

The objective of this research is to develop new neurodynamic programming (NDP) learning algorithm for controlling neuron-level activity (spiking) and synaptic-level plasticity in CMOS/memristor devices, such that the subsequent system-level response achieves desired sensorimotor behavioral goals. The approach is to uses a radically new training paradigm that induces functional plasticity by controlling the neural activity of selected input neurons via programming voltages, rather than by directly manipulating the synaptic weights, as do virtually all existing training algorithms.Intellectual meritThis research aims to develop a model of the closures required to translate synaptic-level plasticity into functional-level plasticity that results into high-level behavioral goals and problem solving abilities. The same critical challenge has been identified in neuroscience research aimed at reverse engineering the brain, and in the regulation of deep-brain stimulation (DBS). Due to this knowledge gap, even when a measure of adequate or desired behavior is available, it may not be easily utilized to stimulate a neural network at the cell level in order to produce the appropriate macroscopic behavior.Broader impactThe learning model developed in this research will be used toward the development of nanoscale neuromorphic systems that mimic neuro-biological architectures in the nervous system. Thanks to their abilities to recreate the synaptic plasticity, device density, scalability, and fault-tolerance of biological neuronal networks, these neuromorphic systems can enable a wide range of technological advancements, such as intelligent robots with highly-sophisticated sensorimotor skills, and neuroprosthetic devices capable of adapting to changing conditions and environments.

本研究的目的是开发新的神经动力学编程（NDP）学习算法，用于控制CMOS/忆阻器器件中的神经元水平活动（尖峰）和突触水平可塑性，从而使随后的系统水平响应达到所需的感觉运动行为目标。 该方法是使用一种全新的训练范式，通过编程电压控制选定输入神经元的神经活动来诱导功能可塑性，而不是直接操纵突触权重，智力价值这项研究的目的是开发一个模型的封闭需要翻译突触水平的可塑性到功能水平的可塑性，从而导致高，水平的行为目标和解决问题的能力。 在旨在对大脑进行逆向工程的神经科学研究以及脑深部电刺激（DBS）的调节中，也发现了同样的关键挑战。 由于这种知识差距，即使当一个足够的或所需的行为的措施是可用的，它可能不容易被用来刺激神经网络在细胞水平上，以产生适当的宏观behavior. broaderimpactThe学习模型在这项研究中开发的将被用于发展的纳米神经形态系统，模仿神经系统中的神经生物学架构。 由于它们能够重建生物神经元网络的突触可塑性，设备密度，可扩展性和容错性，这些神经形态系统可以实现广泛的技术进步，例如具有高度复杂的感觉运动技能的智能机器人，以及能够适应不断变化的条件和环境的神经假体设备。