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

本研究的目的是开发新的神经动力学规划（NDP）学习算法，用于控制CMOS/忆阻器器件中神经元水平的活动（尖峰）和突触水平的可塑性，从而使随后的系统级响应达到预期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，而不是像几乎所有现有的训练算法那样直接操纵突触权重。本研究旨在建立一个将突触水平的可塑性转化为功能水平的可塑性所需的闭包模型，从而产生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中，以及在脑深部刺激（DBS）的调控中，也发现了同样的关键挑战。由于这种知识差距，即使有足够的或期望的行为测量，也可能不容易利用它来刺激细胞水平的神经网络，以产生适当的宏观行为。更广泛的影响本研究中开发的学习模型将用于开发纳米级神经形态系统，模拟神经系统中的神经生物学结构。由于它们能够重建突触可塑性、设备密度、可扩展性和生物神经网络的容错性，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肢设备。

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.