Learning temporal patterns: computational and experimental studies of timing

描述(申请人提供)：人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的皮质处理和由皮质功能异常引起的无数神经疾病至关重要。迈向这一目标的一个必要步骤是了解大脑是如何告诉时间和处理时间信息的。在这里，我们重点研究复杂时空模式的生成和学习问题。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在数百毫秒到几秒的范围内的某种形式的计时的基础，以及最近提出的范式，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，并测试我们假设的明确理论预测。在第二个目标中，我们将开发一个计时的计算模型，作为所提出的范式的实现，以确定它是否能够解释实验结果。 公共卫生相关性：辨别时间和处理时间信息的能力对感觉和运动处理、行为、学习和认知至关重要。越来越明显的是，许多神经疾病--包括学习障碍、帕金森氏症和精神分裂症--的认知异常与正常处理时间信息的能力缺陷有关。因此，无论是正常的大脑功能还是病理的大脑功能，都需要我们揭示大脑能够分辨出 时间到了。目前的项目专注于这个问题，不仅通过直接研究时间处理，而且通过迈出重要的一步，了解复杂的计算是如何从递归神经回路的动力学中产生的。

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.