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

这项研究的目的是开发新的神经动态编程(NDP)学习算法来控制CMOS/Memistor器件中神经元级别的活动(尖峰)和突触级别的可塑性，以便后续的系统级响应达到预期的感觉运动行为目标。这种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通过直接操纵突触权重来诱导功能可塑性。智能价值本研究旨在开发一个闭合模型，将突触水平的可塑性转化为功能水平的可塑性，从而产生高级行为目标和解决问题的能力。在旨在对大脑进行反向工程的神经科学研究中，以及在深部脑刺激(DBS)的监管中，也发现了同样的关键挑战。由于这种知识差距，即使有足够或期望的行为的衡量标准可用，它也可能不容易被用来在细胞水平刺激神经网络以产生适当的宏观行为。广泛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结构。由于它们能够重现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。

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.