CRCNS: Computational Integration of Human Adaptation and Primate Neurophysiology

CRCNS：人类适应和灵长类动物神经生理学的计算整合

• 批准号: 7674660
• 负责人: KURT A THOROUGHMAN
• 金额: $ 32.24万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7674660
• 关键词: Adaptive Behaviors; Affect; Area; Behavior; Behavioral; Brain; Cerebellum; Clinical; Collaborations; Communities; Dependence; Diagnosis; Discipline; Educational process of instructing; Electrical Engineering; Environment; Fostering; Foundations; Hand; Human; Individual; Laboratories; Learning; Life; Mathematics; Mechanics; Metric; Monkeys; Motor; Motor Cortex; Movement; Neurobiology; Neurologic; Neurons; Neurorehabilitation; Output; Patients; Population; Positioning Attribute; Primates; Protocols documentation; Psychology; Rehabilitation therapy; Specificity; Stroke; Students; System; Testing; Training; Trauma; Universities; Vision; Washington; computational neuroscience; computer science; design; experience; haptics; improved; interest; motor control; motor impairment; motor learning; neurophysiology; neuroregulation; nonhuman primate; novel; programs; relating to nervous system; research study; simulation; theories; virtual reality; visual feedback; visual motor

DESCRIPTION (provided by applicant): The neural control of movement encompasses several conundrums in computational neuroscience. The fundamental function of cerebellum and primary and premotor cortices remains elusive. We have recently pioneered behavioral experiments of haptic learning that identify the transformation from sensed movements to incremental adaptations. Simulations have predicted neuronal activities necessary to generate these transformations and have suggested surprising plasticity in the neural representation of movement. Permanent motor impairments due to degeneration, strokes, and traumas affect hundreds of thousands of individuals each year, rendering people unable to perform motor behaviors. A quantitative understanding of normal motor adaptation will enhance the ability of rehabilitation to help patients regain normal function. Here we propose serial human and primate experiments of learning novel visuomotor environments. Humans will experience virtual reality environments that alter the dependence of visual feedback on hand position; the transformation complexity will vary across training days. We will identify how people learn visuomotor transformations over sessions and trial-by-trial, enabling neuronal network simulations to predict neuronal activity needed to mimic human adaptation. We will then record in motor cortex, premotor cortex, and cerebellum as monkeys perform the same visuomotor adaptations, to determine how neuronal activities depend on the environment and on trial-by-trial adaptation. The within-session and across-session changes will test the network simulations and will elucidate the particular contribution of each cortical area as inputs of motor plans, adaptive transformations between vision and action, or outputs of generated movements. The proposed collaboration between Dr. Thoroughman, a computational neuroscientist and psychophysicist, and Dr. Moran, a primate neurophysiologist, will enable a rich connection between neural computation, adaptive behavior, and cortical activity. The new collaboration will directly impact the quality and specificity of leading theories of motor control and learning. We aim to formulate basic scientific foundations that will ultimately improve metrics of neurological diagnosis, inform the design of patientspecific therapies and neuro-rehabilitation protocols, and help patients generalize beyond clinical training to improve motor function in their daily lives.

描述（由申请人提供）：运动的神经控制包括计算神经科学中的几个难题。小脑、初级皮质和前运动皮质的基本功能仍然不清楚。我们最近开创了触觉学习的行为实验，确定从感知运动到增量适应的转变。模拟已经预测了产生这些转换所必需的神经元活动，并表明运动的神经表征具有惊人的可塑性。由于退化、中风和创伤导致的永久性运动障碍每年影响数十万人，使人们无法进行运动行为。对正常运动适应的定量了解将有助于提高康复能力，帮助患者恢复正常功能。 在这里，我们提出了一系列人类和灵长类动物学习新的视觉环境的实验。人类将体验虚拟现实环境，改变视觉反馈对手部位置的依赖性;转换复杂性将在训练日中有所不同。我们将确定人们如何通过会话和逐个试验学习视觉转换，使神经元网络模拟能够预测模拟人类适应所需的神经元活动。然后，我们将在运动皮层、前运动皮层和小脑中记录猴子执行相同的视觉适应，以确定神经元活动如何依赖于环境和逐个试验的适应。会话内和会话间的变化将测试网络模拟，并将阐明每个皮层区域作为运动计划输入、视觉和动作之间的自适应转换或生成运动的输出的特定贡献。Thoroughman博士是一位计算神经科学家和精神病学家，Moran博士是一位灵长类神经生理学家，他们之间的合作将使神经计算、适应行为和皮层活动之间的联系更加丰富。新的合作将直接影响运动控制和学习的领先理论的质量和特异性。我们的目标是制定基本的科学基础，最终改善神经诊断的指标，为患者特定治疗和神经康复方案的设计提供信息，并帮助患者超越临床训练，以改善日常生活中的运动功能。