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: 8825639
• 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/8825639
• 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(由申请人提供) 摘要：大脑不仅是一个非凡的计算器官--能够完成阻碍最好的计算机和机器人的壮举--它还是我们思想和行动的发电机。然而，现代系统神经科学主要问的是大脑如何将输入转化为输出。这种方法有着深厚的历史根源：笛卡尔和谢林顿都将神经系统视为一种精心设计的反射。这种方法也产生了关键的早期成功：芒特卡斯尔、胡贝尔和威塞尔对感觉刺激如何驱动单个神经元反应的描述。然而，大脑显然不仅仅是一种被美化的输入输出设备。它内部的神经网络不仅对外部刺激做出反射性反应，它们还产生自己的ow活动。在这样做的过程中，他们产生了想法、计划、决定和行动。随着对这类过程的研究日益成为系统神经科学的核心，我们将需要越来越关注内部神经动力学：神经电路如何塑造并产生允许我们对世界采取行动的反应。我们将对单个神经元如何反应外部刺激变得不那么感兴趣。随着时间的推移，我们将对神经活动如何维持和塑造自身的动力学产生更大的兴趣。我相信，人们对内部神经动力学的兴趣与日俱增，将推动系统神经科学所采用的概念、分析和实验范式发生重大变化。第一个变化将集中在收集、可视化和分析数据，这些数据可以揭示潜在的动态：神经电路在某个时间点的状态如何合法地导致神经电路在该网络时间点的状态。然后重点将转移到设计最有效地探测动力学的实验上。这样的实验将借鉴物理科学和工程学的技术，但最初将基于系统神经科学的传统行为范式，在这种范式中，动物被训练产生严格控制的行为。然而，我相信传统的实验框架将让位于一个新的框架。我们将直接监控和可操作性地调节内部产生的神经活动本身，而不是通过操作性的条件化行为来间接影响神经活动。这一方法论将建立 基于最近开发的用于神经运动假体的技术平台，但将服务于基本的科学目的：它将使实验者对他们试图了解的系统进行前所未有的控制，并允许对有关动力学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。随后一个平等的目标是利用我们对神经动力学日益增长的理解。我相信，我们应该能够开发出一种新型的神经假体设备，它使用运动皮质活动的动态模式来驱动人工运动。我相信这既是证明你来之不易的动力学知识是有意义的最好方式，也是开发将帮助大量人的神经运动假体的最有效方法之一。

项目成果

A neural network that finds a naturalistic solution for the production of muscle activity.

• DOI: 10.1038/nn.4042
• 发表时间: 2015-07
• 期刊: NATURE NEUROSCIENCE
• 影响因子: 25
• 作者: Sussillo, David;Churchland, Mark M.;Kaufman, Matthew T.;Shenoy, Krishna V.
• 通讯作者: Shenoy, Krishna V.

The Largest Response Component in the Motor Cortex Reflects Movement Timing but Not Movement Type.

• DOI: 10.1523/eneuro.0085-16.2016
• 发表时间: 2016-07
• 期刊: eNeuro
• 影响因子: 3.4
• 作者: Kaufman MT;Seely JS;Sussillo D;Ryu SI;Shenoy KV;Churchland MM
• 通讯作者: Churchland MM

Reorganization between preparatory and movement population responses in motor cortex.

• DOI: 10.1038/ncomms13239
• 发表时间: 2016-10-27
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Elsayed, Gamaleldin F.;Lara, Antonio H.;Kaufman, Matthew T.;Churchland, Mark M.;Cunningham, John P.
• 通讯作者: Cunningham, John P.