Network Models for Timing and Sequence Generation

描述（由申请人提供）：时间是大多数行为和许多认知任务的关键要素，但我们对神经回路如何估计、记忆和控制时间间隔或产生活动的时间序列知之甚少。一类新的网络模型将被研究，显示出很大的希望，揭示的动态机制，在神经电路的水平上运行，支持序列生成和定时计算。这些模型将通过添加调谐反馈回路从通用网络结构构建。这允许同一个网络执行许多不同的任务，类似于学习新任务时发生的现有电路的重新配置。构建执行复杂任务的逼真模型，提高了在模型和真实的神经元网络中运行的动态机制之间建立深层联系的可能性。实现这一承诺需要三个步骤，这就是该提案的三个具体目标。首先，将进行一项研究，以确定哪些运动和认知任务网络模型能够执行，并将其性能水平与动物和人类的性能类似的任务。其次，模型将变得足够现实，以便对实验数据做出明确和预测的陈述。第三，将开发一种方法，使网络模型可以作为一种工具来揭示真实的神经电路中的动态机制。为此，与实验同事建立了合作，从执行延迟到达任务的猴子那里获得多电极记录。成功地实现这三个目标将显着推进理解如何从神经回路动力学的时序产生，并导致电路故障的假设，导致在时序估计和运动启动和控制的缺陷。

DESCRIPTION (provided by applicant): Timing is a critical element of most behaviors and of many cognitive tasks, yet we have little understanding of how neural circuits estimate, remember and control time intervals or generate temporal sequences of activity. A new class of network models will be studied that show great promise for uncovering the dynamic mechanisms, operating at the neural circuit level, supporting sequence generation and timing computations. These models will be built from a generic network structure through the addition of tuned feedback loops. This allows the same network to perform many different tasks and resembles the reconfiguration of existing circuits that occurs when a new task is learned. Constructing realistic models that perform complex tasks raises the possibility of making deep connections between the dynamic mechanisms operating in model and real neuronal networks. Three steps are required to fulfill this promise, and these are the three specific aims of the proposal. First, a study will be undertaken to determine what motor and cognitive tasks network models are capable of performing and to relate their level of performance to that of animals and humans performance analogous tasks. Second, the models will be made realistic enough to make definitive and predicted statements about experimental data. Third, an approach will be developed so that the network models can function as a tool to uncover the dynamic mechanisms operating in real neural circuits. For this purpose a collaboration has been established with experimental colleagues acquiring multi-electrode recordings from monkeys performing delayed-reaching tasks. Successfully accomplishing these three aims will significantly advance understanding of how timing arises from neural circuit dynamics and lead to hypotheses about circuit malfunctions that cause defects in timing estimation and movement initiation and control.