A dynamical systems approach to fundamental questions in neuroscience

神经科学基本问题的动力系统方法

• 批准号: 8605350
• 负责人: Mark Montgomery Churchland
• 金额: $ 8.96万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-30 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8605350
• 关键词: Animals; Behavior; Behavior Control; Behavioral Paradigm; Biological Neural Networks; Brain; Cognition Disorders; Computers; Data Analyses; Devices; Disease; Engineering; Goals; Individual; Knowledge; Life; Limb structure; Locomotion; Methodology; Monitor; Motor; Motor Cortex; Nervous system structure; Neurons; Neurosciences; Organ; Output; Parkinson Disease; Pattern; Plant Roots; Process; Prosthesis; Quadriplegia; Reflex action; Research; Robot; Services; Shapes; Stimulus; System; Techniques; Testing; Thinking; Time; Training; Walking; abstracting; base; conditioning; design; improved; interest; motor disorder; neural circuit; neural patterning; neural prosthesis; neuroregulation; physical science; public health relevance; relating to nervous system; research study; response; sensory stimulus; success

DESCRIPTION (Provided by the applicant) Abstract: The brain is not only a remarkable computational organ - capable of feats that stymie the best computers and robots - it is the generator of our thoughts and actions. Yet modern systems neuroscience has principally asked how the brain transforms inputs into outputs. This approach has deep historical roots: both Descartes and Sherrington saw the nervous system as a massively elaborated reflex. The approach also produced critical early successes: the descriptions by Mountcastle, Hubel, and Wiesel, of how sensory stimuli drive single- neuron responses. Yet the brain is clearly more than a glorified input-output device. The neural networks within it do not just respond reflexively to external stimuli, they also generate their ow activity. In doing so they produce thoughts, plans, decisions and actions. As the study of such processes becomes increasingly central to systems neuroscience, we will need to become increasingly concerned with internal neural dynamics: how neural circuitry shapes and generates the responses that allow us to act upon the world. We will become less interested in how individual neurons reflect external stimuli. We will become much more interested in the dynamics of how neural activity sustains and shapes itself over time. I believe this rising interes in internal neural dynamics will drive large changes in the conceptual, analytical, and experimental paradigms employed by systems neuroscience. The first changes will focus on collecting, visualizing, and analyzing data that can reveal underlying dynamics: how the state of the neural circuit at one point in time leads lawfully to the state of the neural circuit at the net point in time. The focus will then shift to designing experiments that most effectively probe dynamics. Such experiments will borrow techniques from the physical sciences and from engineering, but will initially be based on the traditional behavioral paradigm of systems neuroscience in which animals are trained to produce tightly-controlled behavior. However, I believe the traditional experimental framework will give way to a new one. Instead of indirectly influencing neural activity by operantly conditioning behavior, we will directly monitor and operantly condition the internally generated neural activity itself. This methodology will be built upon the technical platform recently developed in the service of neuro-motor prostheses, but will serve a basic scientific purpose: it will give the experimenter unprecedented control over the system they are trying to understand, and allow stringent tests of hypotheses regarding dynamics. My goal is to help build this emerging paradigm. A subsequent but equal goal is to leverage our growing understanding of neural dynamics. I believe that we should be able to develop a new class of neural prosthetic device that uses the dynamic patterns of motor cortex activity to drive artificial locomotion. I believe this is both the best way to demonstrate that ou hard-won knowledge of dynamics is meaningful, and that it may be one of the most effective ways to develop a neuro-motor prosthesis that will help significant numbers of people.

描述（申请人提供） 摘要：大脑不仅是一个杰出的计算器官 - 能够阻碍最好的计算机和机器人的壮举 - 它是我们思想和动作的发生者。然而，现代系统神经科学主要询问大脑如何将输入转化为输出。这种方法具有深厚的历史根源：笛卡尔和谢灵顿都将神经系统视为详尽的反射。该方法还产生了关键的早期成功：Mountcastle，Hubel和Wiesel的描述，即感觉刺激如何驱动单神经元反应。然而，大脑显然不仅仅是一个荣耀的输入输出设备。其中的神经网络不仅对外部刺激做出反思，还会产生OW活动。在这样做的过程中，他们会产生思想，计划，决策和行动。随着对此类过程的研究变得越来越核心，我们将需要越来越关注内部神经动态：神经回路的形状如何并产生使我们能够对世界采取行动的反应。我们将对单个神经元如何反映外部刺激的感兴趣。随着时间的流逝，我们将对神经活动如何维持和塑造自身的动态变得更加感兴趣。我认为，内部神经动力学中的这种上升会推动系统神经科学采用的概念，分析和实验范式的巨大变化。第一个更改将集中于收集，可视化和分析可以揭示基本动态的数据：在某个时间点神经回路状态如何在净时间净点上合法地导致神经回路状态。然后，重点将转向设计最有效探测动力学的实验。这样的实验将从物理科学和工程学中借用技术，但最初将基于系统神经科学的传统行为范式，在这些行为上，对动物进行了训练以产生紧密控制的行为。但是，我相信传统的实验框架将被一个新的实验框架所取代。我们将直接监测并操纵内部生成的神经活动本身，而不是通过操纵行为来间接影响神经活动。将建立此方法 在最近为神经运动假体服务的技术平台上开发的技术平台，但将实现一个基本的科学目的：它将为实验者提供对他们试图理解的系统的前所未有的控制，并允许对动态的假设进行严格的测试。我的目标是帮助建立这个新兴的范式。随后但平等的目标是利用我们对神经动态的不断增长的理解。我相信，我们应该能够开发一种新的神经假体装置，该设备使用运动皮层活动的动态模式来驱动人造运动。我认为这既是证明动态知识的最佳方法是有意义的，也可能是开发神经运动假体的最有效方法之一，这将有助于大量人。