Neural mechanisms and behavioral consequences of non-Gaussian likelihoods in sensorimotor learning

感觉运动学习中非高斯可能性的神经机制和行为后果

• 批准号: 9170650
• 负责人: Ilya M. Nemenman
• 金额: $ 34.54万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9170650
• 关键词: Accounting; Acoustics; Adult; Age; Animal Behavior; Animals; Anterior; Bayesian Analysis; Behavior; Behavioral; Behavioral Model; Biological; Biological Models; Brain; Cell Nucleus; Communication; Communities; Complex; Computer Simulation; Computer software; Data; Data Analyses; Data Set; Databases; Dependence; Development; Educational workshop; Electrophysiology (science); Ensure; Environment; Formulation; Frequencies; Future; Genetic Programming; Goals; Human; Individual; Lateral; Learning; Likelihood Functions; Literature; Maintenance; Markov chain Monte Carlo methodology; Modeling; Motor; Motor output; Muscle; Nervous System Trauma; Nervous system structure; Neurons; Neurosciences; Noise; Output; Patients; Pattern; Physiological; Population; Process; Prosencephalon; Publishing; Rehabilitation therapy; Reporting; Research; Sampling; Sensory; Sensory Process; Shapes; Signal Transduction; Songbirds; Speed; Study models; System; Tail; Techniques; Testing; Time; Update; Validation; base; bird song; computer based statistical methods; critical period; design; flexibility; improved; innovation; learned behavior; learning ability; magnocellular; mathematical methods; mathematical theory; member; motor learning; nervous system disorder; neural circuit; neural correlate; neuromechanism; neurophysiology; novel; relating to nervous system; research study; response; sensory feedback; sensory input; skills; symposium; theories; tool; vocal learning

A central goal of neuroscience is to understand how learning is implemented by the nervous system. However, despite years of studies in animals and humans, our understanding of both the computational basis of learning and its implementation by the brain is still rudimentary. A critical gap therefore exists between the large amount of behavioral and neural data that has been collected during learning and a mathematical and biological understanding of the rules governing motor plasticity. This proposal will develop a unified mathematical theory for understanding how the brain learns complex skills. The theoretical framework will be implemented in software and will be applicable to and validated on a wide variety of sensorimotor data. The primary experimental validation system will be songbirds, which provide a physiologically accessible model system to investigate sensorimotor learning. Our objective in the songbird system is to understand sensorimotor learning of a single acoustic parameter – fundamental frequency (pitch) – which is known to be precisely regulated by the songbird brain. Our central hypothesis is that learning is implemented as a Bayesian inference, and that the stochastic sampling of motor commands from the current Bayesian a priori distribution of outputs is coordinated by a network of neurons in the forebrain. Drawing on a large quantity of both theoretical and experimental results, two specific aims will test this hypothesis. The first aim will introduce an innovative new class of computational model in which the brain uses an iterative process of Bayesian inference to reshape behavior in response to sensory feedback. The models will be validated using population-averaged animal behavior. The second aim will analyze data recorded from individual animals and single neurons in behaving animals to identify the biological mechanisms underlying sensorimotor learning. Throughout, we will design, test, and make public software that will allow other members of the community to apply our novel tools to their own data. Our approach is innovative because it will provide a unified framework for understanding the results of a wide variety of behavioral and neural studies across both tasks and species. These studies are significant because a better understanding of the mechanisms underlying sensorimotor learning could aid in the design of rehabilitative strategies that exploit the plasticity of complex behavior.

神经科学的一个中心目标是了解神经系统如何实现学习。 然而，尽管对动物和人类进行了多年的研究，我们对计算基础的理解 因此，大脑的学习能力及其实现仍处于初级阶段。 在学习过程中收集的大量行为和神经数据以及数学和 该提案将制定统一的运动可塑性规则的生物学理解。 用于理解大脑如何学习复杂技能的数学理论的理论框架。 将在软件中实现，并将适用于各种感觉运动数据并对其进行验证。 主要的实验验证系统将是鸣禽，它提供了生理上可访问的 研究感觉运动学习的模型系统 我们在鸣禽系统中的目标是理解。 单一声学参数的感觉运动学习——基频（音调）——众所周知 我们的中心假设是，学习是通过贝叶斯模型实现的。 推断，以及从当前贝叶斯先验分布中对运动命令进行随机采样 输出是由前脑中的神经元网络协调的。 理论和实验结果，两个具体目标将检验这一假设。 创新的新型计算模型，其中大脑使用贝叶斯推理的迭代过程 重塑行为以响应感官反馈 该模型将使用人口平均值进行验证。 第二个目标是分析从个体动物和单个神经元记录的数据。 我们将通过观察动物的行为来识别感觉运动学习的生物机制。 设计、测试和制作公共软件，使社区的其他成员能够应用我们的新颖工具 我们的方法是创新的，因为它将提供一个统一的框架来理解 这些研究是跨任务和物种的各种行为和神经研究的结果。 意义重大，因为更好地理解感觉运动学习的机制可能有助于 利用复杂行为的可塑性的康复策略的设计。