Network Models for Timing and Sequence Generation

用于定时和序列生成的网络模型

• 批准号: 8260840
• 负责人: Laurence F. Abbott
• 金额: $ 39.07万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8260840
• 关键词: Animals; Behavior; Brain; Code; Cognitive; Collaborations; Complex; Data; Data Quality; Defect; Electrodes; Elements; Experimental Models; Feedback; Generations; Generic Drugs; Heterogeneity; Human; Individual; Knowledge; Lead; Learning; Methods; Modeling; Modification; Monkeys; Motor; Motor Cortex; Movement; Muscle; Neural Network Simulation; Neurons; Output; Parkinson Disease; Performance; Population; Process; Property; Recurrence; Research; Signal Transduction; Solutions; Structure; Suggestion; System; Time; Variant; Work; biological systems; data modeling; expectation; interest; network models; neural circuit; nonhuman primate; public health relevance; relating to nervous system; response; time interval; tool

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. PUBLIC HEALTH RELEVANCE: The proposed research will use network models and parallel analyses of models and experimental data to reveal the dynamic mechanisms by which motor sequences are initiated and carried out and other timing-related tasks are performed. Parkinson's disease is characterized by difficulty in initiating voluntary movements and by abnormalities of timing estimation. This work thus has the potential to illuminate our understanding of motor and cognitive tasks involving timing in both healthy and diseased states.

描述(申请人提供)：计时是大多数行为和许多认知任务的关键要素，但我们对神经电路如何估计、记忆和控制时间间隔或产生活动的时间序列知之甚少。将研究一类新的网络模型，这些模型在揭示动态机制、在神经电路级别操作、支持序列生成和定时计算方面具有巨大的前景。这些模型将通过添加调谐反馈环路从通用网络结构中构建。这允许同一网络执行许多不同的任务，类似于在学习新任务时对现有电路进行重新配置。构建执行复杂任务的现实模型，提高了在模型中运行的动态机制与真实神经元网络之间建立深层联系的可能性。实现这一承诺需要三个步骤，这是该提案的三个具体目标。首先，将进行一项研究，以确定网络模型能够执行哪些运动和认知任务，并将它们的性能水平与动物和人类执行类似任务的水平联系起来。其次，模型将变得足够现实，能够对实验数据做出确定性和预测性的陈述。第三，将开发一种方法，使网络模型能够作为工具来揭示真实神经电路中运行的动态机制。为此，已经与实验同事建立了合作关系，从执行延迟到达任务的猴子那里获取多电极记录。成功地实现这三个目标将极大地促进对计时如何从神经电路动力学中产生的理解，并导致关于电路故障的假说，这些故障导致在计时估计和运动启动和控制方面的缺陷。 公共卫生相关性：拟议的研究将使用网络模型以及对模型和实验数据的并行分析来揭示启动和执行运动序列以及执行其他与时间相关的任务的动态机制。帕金森氏症的特点是难以启动自主运动和时间估计的异常。因此，这项工作有可能阐明我们对健康和疾病状态下涉及计时的运动和认知任务的理解。