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（由申请人提供）：智力优点：感知决策是必不可少的认知能力。它需要可以积累感官证据的神经回路，将其与先前的信息结合在一起，然后在适当的时间选择适当的动作。大脑执行这些计算能力的理论主要涉及基于动力学系统的“机械”模型，或者是从心理学，统计或经济学中进口的最佳决策模型的“规范性”模型。但是，现有理论没有考虑到被认为执行这些计算的神经元的详细响应属性。评估此类理论所需的多神经元录音尚未收集。目前尚无一般理论框架来将涉及决策中涉及的各种感觉，运动，记忆和奖励变量与多个共同计算决策的多个神经元的跨度尖峰响应。该建议旨在填补这一空白。拟议的研究的目的是对侧向内皮层（区域LIP）中神经元组对决策相关信息的编码和解码的详细而全面的了解，这是一个大脑区域，这是一个与决策强烈牵涉的大脑区域。将从从事决策任务的灵长类动物那里获得多电极记录；这将为尖峰神经元组同时代表决策的第一个窗口。研究人员将开发一个高度灵活的概率尖峰火车模型，以捕获唇部神经种群的尖峰反应，并结合神经元，峰值历史和适应性之间的相关性，以及一组对各种感觉，运动，决策，决策和奖励变量的依赖性。这项研究的一个新特征是，它不会以唇部功能的特定机械或规范理论为前提。相反，它首先要寻求一种描述性的唇部反应模型，因为它们实际上存在于大脑中。这将允许对唇尖端携带的时变信息以及解码它们的各种策略的最佳性进行完整的核算，并将提供一个平台，用于得出和评估唇部功能的简化模型。这项研究将紧密整合理论，并与几个旨在检查多个神经元决策的联合编码的新实验进行实验。协作：拟议的研究代表了两位具有计算神经科学和系统神经生理学专业知识的年轻研究者之间的新合作。它将结合用于尖峰火车建模的最新统计方法和记录多个神经元活性的实验方法。该提案的目标将通过将理论和模型发展与电生理实验紧密相结合，这将通过两个研究者的接近度来促进。更广泛的影响：顶叶皮层在决策中起着核心作用，并涉及各种主要的脑部疾病，包括抑郁症，焦虑症，精神分裂症和帕金森氏病。通过揭示健康大脑中神经决策的计算基础，拟议的研究具有巨大的希望，可以促进对这些疾病的理解和治疗。此外，要开发的模型和方法非常笼统，适用于参与感觉和运动处理的各种大脑区域。这些方法将有助于设计高级感觉和运动神经假体设备，即替代损坏的感觉或运动系统受损部分的人体工程系统。所有软件将在线公开提供，这将增强计算神经科学研究和教育的基础架构。该研究建议将在几个关键方面促进教学和培训。该项目从根本上是跨学科的，结合了尖端的生理和计算技术。学员将在两个调查员的实验室里度过时光，并将获得无价的动手合作教育。该项目还将直接告知两位研究人员开发的课程。调查人员将通过对学习盟友的视力障碍的基本数学和科学教科书进行录音来促进公众的科学理解（奥斯汀的录音室对视觉障碍障碍）。调查人员将旨在招募传统代表性不足的群体，尤其是妇女的实习生和毕业生。最后，他们将在当地的中学和高中进行宣传，以激发人们对数学和计算机科学的热情，这是发现大脑如何工作的令人兴奋的挑战的基础。