Learning temporal patterns: computational and experimental studies of timing

描述（由申请人提供）：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皮层处理和由异常皮层功能产生的无数神经系统疾病至关重要。实现这一目标的必要步骤将是了解大脑如何告诉时间和处理时间信息。在这里，我们关注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：循环神经网络的内部动态构成了数百毫秒到几秒范围内某些形式的定时，以及最近提出的范式，即时间被编码在神经元群体不断变化的活动模式中。这项建议包括两个目标。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，我们将开发一个时间的计算模型，作为所提出范式的实现，以确定它是否可以解释实验结果。

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.