Control of Dynamic Patterns in Neuronal Networks

神经网络动态模式的控制

• 批准号: 1509342
• 负责人: Jr-Shin Li
• 金额: $ 47.67万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1509342&HistoricalAwards=false
• 关键词: Control; Dynamic; Patterns; Neuronal; Networks

The past decade has seen significant growth in the development and use of neurostimulation technology to manipulate neural activity in the brain. The applications of such technology range from scientific objectives, e.g., studying how different parts of the brain interact with each other, to clinical objectives, e.g., using stimulation to alleviate the symptoms of neurological disorders such as Parkinson's disease. Despite many technological advances associated with such stimulation, its use is still largely limited to perturbative paradigms, in which stereotyped inputs (waveforms) are used to activate or deactivate a neuronal network in its entirety. In other words, the stimulation is used to create a uniform circuit response, turning an entire population of brain cells (neurons) on or off, without regard for specificity (i.e., which cells in the population respond) or timing (i.e., when they turn on or off). In engineering, control is understood as not simply uniform stimulation, but as the precise creation or prevention of certain system maneuvers at each moment in time. This project will investigate fundamental questions regarding the use of neurostimulation to control neuronal networks in this temporally precise sense. That is, rather than simply stimulating the brain, the goal of this research is to develop new engineering theory and methods to allow practitioners to steer the activity in neural circuits so as to create complex patterns of activity, or neural spiking. Thus, this highly transdisciplinary project will elucidate enabling theory for the use of neurostimulation and will lead to new and fundamental contributions to systems theory and control engineering. The project will also support new initiatives to promote interdisciplinary education for students from traditionally underserved populations through the creation of summer workshops for students from local high schools in the city of St. Louis, MO.By bridging ensemble systems theory with computational neuroscience, general and versatile frameworks for neuronal control will be formulated. Specifically, oscillator and conductance-based neural models will be used to mathematically model both oscillatory and non-oscillatory regimes in brain networks. Using these models, the proposed work will determine fundamental limits on the controllability of neuronal spiking or synchronization by the application of external inputs. The notion of ensemble reachability is also proposed and will be examined via entropic gain analysis and dynamic optimization. These characterizations of fundamental control properties in both oscillatory and non-oscillatory neural dynamic regimes will then facilitate the development of control design paradigms to synthesize optimal controls for the creation of complex patterns in neuronal populations, such as firing or entrainment patterns. Methods of ensemble control and formal averaging will be employed to derive optimal sequence and pattern controls, and stochastic versions of these problems will also be treated using stochastic control techniques to ensure tolerance to noise and uncertainty, which are pervasive in neural circuits. Thus, the results of the proposed research will include a unified systems-theoretic framework for analyzing the control of physiologically relevant brain networks; and further, will include a set of formal neural control design methods that may be readily translated to a range of neurostimulation implementations.

过去十年，用于操纵大脑神经活动的神经刺激技术的开发和使用取得了显着增长。 此类技术的应用范围从科学目标（例如，研究大脑的不同部分如何相互作用）到临床目标（例如，使用刺激来减轻帕金森病等神经系统疾病的症状）。尽管与这种刺激相关的技术取得了许多进步，但其使用仍然很大程度上局限于微扰范式，其中使用刻板输入（波形）来激活或停用整个神经元网络。换句话说，刺激用于创建统一的电路响应，打开或关闭整个脑细胞（神经元）群体，而不考虑特异性（即群体中的哪些细胞做出反应）或时间安排（即它们何时响应）。打开或关闭）。在工程学中，控制不仅被理解为简单的均匀刺激，而且被理解为在每个时刻精确地创建或阻止某些系统操作。该项目将研究有关使用神经刺激在时间精确意义上控制神经元网络的基本问题。也就是说，这项研究的目标不是简单地刺激大脑，而是开发新的工程理论和方法，让实践者能够引导神经回路的活动，从而创建复杂的活动模式或神经尖峰。因此，这个高度跨学科的项目将阐明使用神经刺激的使能理论，并将为系统理论和控制工程带来新的基础性贡献。该项目还将支持新举措，通过为密苏里州圣路易斯市当地高中的学生举办夏季讲习班，促进传统上服务不足人群的学生的跨学科教育。将制定神经元控制的通用框架。具体来说，基于振荡器和电导的神经模型将用于对大脑网络中的振荡和非振荡状态进行数学建模。使用这些模型，拟议的工作将确定通过应用外部输入对神经元尖峰或同步的可控性的基本限制。还提出了集合可达性的概念，并将通过熵增益分析和动态优化进行检查。振荡和非振荡神经动态机制中基本控制特性的这些特征将促进控制设计范式的发​​展，以综合最佳控制以在神经元群体中创建复杂模式，例如激发或夹带模式。将采用集成控制和形式平均的方法来导出最佳序列和模式控制，并且还将使用随机控制技术来处理这些问题的随机版本，以确保对神经回路中普遍存在的噪声和不确定性的容忍度。 因此，拟议研究的结果将包括一个统一的系统理论框架，用于分析生理相关大脑网络的控制；此外，还将包括一组正式的神经控制设计方法，可以很容易地转化为一系列神经刺激实现。