CRCNS Detailed multi-neuron coding of decisions in parietal cortex

CRCNS 顶叶皮层决策的详细多神经元编码

• 批准号: 8443949
• 负责人: Alexander C Huk
• 金额: $ 27.37万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8443949
• 关键词: Accounting; Address; Algorithms; Ally; Animals; Anxiety; Area; Behavior; Behavioral; Brain; Brain Diseases; Brain region; Code; Cognitive; Collaborations; Complex; Computational Technique; Computer software; Data; Decision Making; Dependency; Development; Diffusion; Discipline; Discrimination; Disease; Economics; Education; Educational process of instructing; Electrodes; Environment; Exhibits; Experimental Models; Eye; Fostering; Functional disorder; Goals; Human Engineering; Individual; Instruction; Joints; Lateral; Learning; Literature; Location; Mathematics; Memory; Mental Depression; Mental disorders; Methodology; Methods; Modeling; Monkeys; Motor; Neurons; Parietal Lobe; Parkinson Disease; Pattern; Physiological; Play; Population; Positioning Attribute; Postdoctoral Fellow; Primates; Process; Property; Prosthesis; Psyche structure; Psychology; Reading; Recording of previous events; Recruitment Activity; Refractory; Research; Research Infrastructure; Research Personnel; Research Proposals; Rewards; Role; Saccades; Sampling; Schizophrenia; Science; Scientist; Sensory; Short-Term Memory; Side; Signal Transduction; Software Tools; Statistical Methods; Statistical Models; Stimulus; Structure; Students; System; Techniques; Testing; Textbooks; Time; Training; Translating; Visual; Visual impairment; Woman; Work; analytical tool; austin; base; computational neuroscience; computer code; computer science; design; experience; flexibility; high school; information processing; interest; lateral intraparietal area; lectures; meetings; member; middle school; model development; neural circuit; neural prosthesis; neurophysiology; novel; outreach; relating to nervous system; research study; response; software development; statistics; theories

DESCRIPTION (provided by applicant): Intellectual Merit: Perceptual decision-making is an essential cognitive capability. It requires neural circuits that can accumulate sensory evidence, combine it with prior information, and select an appropriate action at an appropriate time. Theories of the brain's ability to perform these computations have primarily involved either "mechanistic" models based on dynamical systems, or "normative" models of optimal decision-making imported from psychology, statistics, or economics. However, existing theories do not account-or even attempt to account-for the detailed response properties of neurons believed to carry out these computations. Multi-neuron recordings necessary to evaluate such theories have not yet been collected. There is as yet no general theoretical framework for relating the various sensory, motor, memory, and reward variables involved in decision-making to the time-varying spike responses of multiple neurons that collectively compute decision. This proposal aims to fill that gap. The goal of the proposed research is a detailed and comprehensive understanding of the encoding and decoding of decision-related information by groups of neurons in lateral intraparietal cortex (area LIP), a brain region strongly implicated in decision-making. Multi-electrode recordings will be obtained from primates engaged in decision-making tasks; this will provide the first window into the simultaneous representation of decisions by groups of spiking neurons. The investigators will develop a highly flexible probabilistic spike train model to capture the spike responses of neural populations in LIP, incorporating correlations between neurons, spike-history and adaptation, and a complete set of dependencies on various sensory, motor, decision and reward variables. A novel feature of this research is that it does not presuppose a particular mechanistic or normative theory of LIP function; rather, it begins by seeking a descriptive model of LIP responses as they actually exist in the brain. This will allow for a full accounting of the time-varying information carried by LIP spikes and the optimality of various strategies for decoding them, and will provide a platform for deriving and evaluating simplified models of LIP function. The research will tightly integrate theory and experiment with several new experiments designed to examine the joint coding of decisions across multiple neurons. Collaboration: The proposed research represents a new collaboration between two young investigators with expertise in computational neuroscience and systems neurophysiology. It will combine state-of-the-art statistical methods for spike train modeling and experimental methods recording the simultaneous activity of multiple neurons. The goals of the proposal will be met by closely integrating theory and model development with electrophysiological experiments, which will be facilitated by the proximity of the two investigators. Broader Impacts: The parietal cortex plays a central role in decision-making, and is implicated in a variety of major brain disorders, including depression, anxiety, schizophrenia, and Parkinson's disease. By revealing the computational underpinnings of neural decision making in healthy brains, the proposed research holds great promise for advancing the understanding and treatment of these disorders. Moreover, the models and methodologies to be developed are very general, with applicability to a wide variety of brain areas involved in sensory and motor processing. These methods will aid in the design of advanced sensory and motor neural prosthetic devices, human-engineered systems that replace damaged portions of the sensory or motor system. All software will be made publicly available online, which will enhance the infrastructure for research and education in computational neuroscience. The research proposal will promote teaching and training in several key respects. The project is fundamentally interdisciplinary, combining cutting-edge physiological and computational techniques. Trainees will spend time in both investigator's labs, and will receive an invaluable hands-on, collaborative education. The project will also directly inform classes developed by both investigators. The investigators will promote public scientific understanding by making audio recordings of basic math and science textbooks for the visually impaired at the Learning Ally (Austin's recording studio for the visually impaired). The investigators will aim to recruit interns and graduates from traditionally under-represented groups, especially women. Finally, they will conduct outreach at local middle and high schools in order to spark enthusiasm for mathematics and computer science, disciplines which are fundamental to the exciting challenge of discovering how the brain works.

描述(申请人提供)：智力优点：知觉决策是一种基本的认知能力。它需要神经回路，可以积累感觉证据，将其与先前的信息结合起来，并在适当的时间选择适当的行动。关于大脑执行这些计算能力的理论主要涉及基于动力系统的“机械”模型，或从心理学、统计学或经济学引入的最佳决策的“标准”模型。然而，现有的理论没有解释--甚至试图解释--被认为进行这些计算的神经元的详细反应特性。评估这些理论所需的多神经元记录尚未收集到。到目前为止，还没有一个通用的理论框架来将决策中涉及的各种感觉、运动、记忆和奖励变量与共同计算决策的多个神经元的时变尖峰反应联系起来。这项提议旨在填补这一空白。这项拟议的研究的目标是详细和全面地了解大脑中与决策密切相关的大脑区域--外侧顶内皮质(LIP区)的神经元群对决策相关信息的编码和解码。多电极记录将从参与决策任务的灵长类动物那里获得；这将提供第一个窗口，让人们了解一组尖峰神经元同时表示决策的情况。研究人员将开发一个高度灵活的概率棘波训练模型来捕捉嘴唇中神经种群的棘波反应，包括神经元之间的关联、棘波历史和适应，以及对各种感觉、运动、决策和奖励变量的完整依赖。这项研究的一个新特点是，它不假设嘴唇功能的特定机械或规范理论；相反，它从寻找嘴唇反应的描述性模型开始，因为它们实际上存在于大脑中。这将允许充分考虑嘴唇尖峰所携带的时变信息以及解码它们的各种策略的最佳性，并将为推导和评估嘴唇功能的简化模型提供一个平台。这项研究将把理论和实验与几个新的实验紧密结合起来，这些实验旨在检查跨多个神经元的决策的联合编码。合作：这项拟议的研究代表了两名在计算神经科学和系统神经生理学方面拥有专业知识的年轻研究人员之间的新合作。它将结合最先进的棘波训练建模统计方法和记录多个神经元同时活动的实验方法。该提案的目标将通过将理论和模型开发与电生理实验紧密结合起来实现，这将因两名研究人员的接近而得到促进。更广泛的影响：顶叶皮质在决策中发挥着核心作用，并与各种主要的大脑疾病有关，包括抑郁、焦虑、精神分裂症和帕金森氏症。通过揭示健康大脑中神经决策的计算基础，拟议的研究为促进对这些疾病的理解和治疗带来了巨大的希望。此外，将要开发的模型和方法非常通用，适用于涉及感觉和运动处理的各种大脑区域。这些方法将有助于设计先进的感觉和运动神经假体设备，这是一种人类工程系统，可以取代感觉或运动系统的受损部分。所有软件都将在网上公开使用，这将增强计算神经科学研究和教育的基础设施。该研究提案将在几个关键方面促进教学和培训。该项目基本上是跨学科的，结合了尖端的生理学和计算机技术。受训者将在两个研究人员的实验室中花费时间，并将接受宝贵的实践和协作教育。该项目还将直接向两位研究人员开发的课程提供信息。调查人员将在学习小巷(奥斯汀的视障人士录音棚)为视障人士制作基本数学和科学教科书的音频，以促进公众对科学的理解。调查人员的目标将是从传统上代表性不足的群体招聘实习生和毕业生，特别是女性。最后，他们将在当地的初中和高中开展宣传活动，以激发人们对数学和计算机科学的热情，这两门学科是探索大脑如何工作的激动人心的挑战的基础。