Learning temporal patterns: computational and experimental studies of timing

学习时间模式：时间的计算和实验研究

• 批准号: 8385396
• 负责人: DEAN V BUONOMANO
• 金额: $ 7.7万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8385396
• 关键词: Accounting; Behavior; Biological Neural Networks; Brain; Cerebral cortex; Cognition; Cognitive; Complex; Computer Simulation; Epilepsy; Event; Exhibits; Generations; Goals; Human; Learning; Learning Disabilities; Modeling; Motor; Neurons; Parkinson Disease; Pattern; Performance; Population; Process; Property; Psychophysiology; Recurrence; Research; Runaway; Schizophrenia; Sensory; Synapses; System; Testing; Time; Uncertainty; Weight; base; computer studies; design; man; millisecond; nervous system disorder; neural circuit; neuromechanism; novel; prevent; relating to nervous system; research study; response; spatiotemporal; time interval

DESCRIPTION (provided by applicant): The human brain remains the most sophisticated computational system known to man. Elucidating the mechanisms underlying the cerebral cortex's ability to generate behavior and cognition is critical for understanding both normal cortical processing and a myriad of neurological disorders produced by abnormal cortical function. A necessary step towards this goal will be to understand how the brain tells time and processes temporal information. Here we focus on the problem of generating and learning complex spatiotemporal patterns. The studies proposed here are based on the hypothesis that the internal dynamics of recurrent neural networks underlies some forms of timing in the range of hundreds of milliseconds to a few seconds, and on the recently proposed paradigm that time is encoded in the continuously changing activity pattern of a neuronal population. The proposal consists of two aims. In the first we will use a novel human psychophysical task to study the learning of temporal patterns and test explicit theoretical predictions of our hypothesis. In the second aim we will develop a computational model of timing as an implementation of the proposed paradigm, to determine whether it can account for the experimental results. PUBLIC HEALTH RELEVANCE: The ability to tell time and process temporal information is of fundamental importance to sensory and motor processing, behavior, learning, and cognition. And it is increasingly clear that the cognitive abnormalities in a number of neurological diseases-including learning disabilities, Parkinson's disease, and schizophrenia- are associated with deficits in the ability to normally process temporal information. Thus elucidating both normal and pathological brain function will require that we unveil the mechanisms that allow the brain to tell time. The current project focuses on this problem, not only by directly studying temporal processing, but by taking the important step towards understanding how complex computations emerge from the dynamics of recurrent neural circuits.

描述（由申请人提供）：人类大脑仍然是人类已知的最复杂的计算系统。阐明大脑皮层产生行为和认知能力的机制对于理解正常皮层处理和由异常皮层功能产生的无数神经障碍是至关重要的。实现这一目标的必要步骤是了解大脑如何判断时间和处理时间信息。在这里，我们专注于生成和学习复杂的时空模式的问题。这里提出的研究是基于这样的假设，即递归神经网络的内部动力学是数百毫秒到几秒范围内的某种形式的计时的基础，并且基于最近提出的范式，即时间被编码在神经元群体的连续变化的活动模式中。该提案包括两个目标。首先，我们将使用一种新的人类心理物理任务来研究时间模式的学习，并测试我们的假设的明确的理论预测。在第二个目标中，我们将开发一个计算模型的时间作为所提出的范例的实施，以确定它是否可以解释实验结果。 公共卫生相关性：分辨时间和处理时间信息的能力对于感觉和运动处理、行为、学习和认知具有根本的重要性。越来越清楚的是，许多神经系统疾病（包括学习障碍、帕金森病和精神分裂症）的认知异常与正常处理时间信息的能力缺陷有关。因此，要阐明正常和病理的大脑功能，就需要我们揭开大脑的机制， 时间目前的项目集中在这个问题上，不仅通过直接研究时间处理，而且通过了解复杂计算如何从递归神经回路的动态中出现迈出重要的一步。