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

基本信息

批准号：
8660348
负责人：
Alexander C Huk
金额：
$ 24.81万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
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/8660348
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 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 junior high school lateral intraparietal area lectures mathematical sciences meetings member model development neural circuit neural prosthesis neurophysiology novel outreach relating to nervous system research study response software development statistics theories

项目摘要

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：感知决策是一种重要的认知能力。它需要神经回路能够积累感官证据，将其与先验信息相结合，并在适当的时间选择适当的行动。关于大脑执行这些计算的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“规范”模型。然而，现有的理论没有解释——甚至没有试图解释——被认为执行这些计算的神经元的详细反应特性。评估此类理论所需的多神经元记录尚未收集。目前还没有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响应联系起来。该提案旨在填补这一空白。本研究的目标是详细、全面地了解外侧顶叶皮层（LIP 区）神经元群对决策相关信息的编码和解码，该区域是与决策密切相关的大脑区域。多电极记录将从从事决策任务的灵长类动物身上获得；这将为同时表征尖峰神经元组的决策提供第一个窗口。研究人员将开发一种高度灵活的概率尖峰训练模型，以捕获 LIP 中神经群体的尖峰反应，结合神经元、尖峰历史和适应之间的相关性，以及对各种感觉、运动、决策和奖励变量的完整依赖关系。这项研究的一个新颖之处在于，它并不预设 LIP 功能的特定机制或规范理论。相反，它首先寻找大脑中实际存在的唇部反应的描述性模型。这将允许全面考虑 LIP 尖峰携带的时变信息以及解码它们的各种策略的最优性，并将为推导和评估 LIP 函数的简化模型提供一个平台。该研究将理论和实验与几个旨在检查多个神经元决策的联合编码的新实验紧密结合起来。合作：拟议的研究代表了两位在计算神经科学和系统神经生理学方面具有专业知识的年轻研究人员之间的新合作。它将结合最先进的尖峰序列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学实验紧密结合来实现，这将通过两位研究人员的接近而促进。更广泛的影响：顶叶皮层在决策中发挥着核心作用，并与多种主要的大脑疾病有关，包括抑郁症、焦虑症、精神分裂症和帕金森病。通过揭示健康大脑中神经决策的计算基础，拟议的研究为推进对这些疾病的理解和治疗带来了巨大的希望。此外，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装置，以及替代感觉或运动系统受损部分的人体工程系统。所有软件都将在网上公开提供，这将增强计算神经科学研究和教育的基础设施。该研究计划将促进几个关键方面的教学和培训。该项目从根本上来说是跨学科的，结合了尖端的生理学和计算技术。学员将在两个研究人员的实验室中度过一段时间，并将接受宝贵的实践、协作教育。该项目还将直接为两位研究人员开发的课程提供信息。研究人员将在 Learning Ally（奥斯汀视障人士录音室）为视障人士录制基础数学和科学教科书的录音，以促进公众对科学的理解。调查人员的目标是从传统上代表性不足的群体中招募实习生和毕业生，尤其是女性。最后，他们将在当地初中和高中进行推广活动，以激发人们对数学和计算机科学的热情，这些学科是探索大脑如何运作的令人兴奋的挑战的基础。