CRCNS: Multiple clocks for the encoding of time in corticostriatal circuits

CRCNS：皮质纹状体电路中时间编码的多个时钟

• 批准号: 10396146
• 负责人: DEAN V BUONOMANO
• 金额: $ 37.68万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10396146
• 关键词: Address; Animals; Area; Automobile Driving; Behavior; Behavioral; Brain; Cerebellar Nuclei; Code; Computer Models; Corpus striatum structure; Cues; Data; Electrophysiology (science); Event; Food; Generations; Goals; Hippocampus (Brain); Huntington Disease; Impairment; Instruction; Interneurons; Learning; Life; Link; Medial; Modeling; Motor; Motor Cortex; Movement; Mus; Neural Network Simulation; Neurosciences; Parkinson Disease; Parvalbumins; Pattern; Phase; Play; Prefrontal Cortex; Property; Reading; Recovery; Recurrence; Rewards; Role; Shapes; Signal Transduction; Speed; Supervision; Surveys; System; Testing; Time; Work; data modeling; entorhinal cortex; experimental study; flexibility; innovation; insight; learned behavior; nervous system disorder; neural patterning; optogenetics; recurrent neural network; relating to nervous system; response; Address; Animals; Area; Automobile Driving; Behavior; Behavioral; Brain; Cerebellar Nuclei; Code; Computer Models; Corpus striatum structure; Cues; Data; Electrophysiology (science); Event; Food; Generations; Goals; Hippocampus (Brain); Huntington Disease; Impairment; Instruction; Interneurons; Learning; Life; Link; Medial; Modeling; Motor; Motor Cortex; Movement; Mus; Neural Network Simulation; Neurosciences; Parkinson Disease; Parvalbumins; Pattern; Phase; Play; Prefrontal Cortex; Property; Reading; Recovery; Recurrence; Rewards; Role; Shapes; Signal Transduction; Speed; Supervision; Surveys; System; Testing; Time; Work; data modeling; entorhinal cortex; experimental study; flexibility; innovation; insight; learned behavior; nervous system disorder; neural patterning; optogenetics; recurrent neural network; relating to nervous system; response

The ability to predict when external events will occur, such as anticipating the actions of a predator or the availability of food, is critical for survival. Converging computational and experimental work suggests that dynamically changing patterns of neural activity, including neural sequences, underlie temporal prediction and temporal processing. It is increasingly clear that timing and temporal prediction are highly distributed computations, however, there has been little effort to systematically contrast and understand the computational tradeoffs between how time is encoded in different brain areas. Furthermore, while converging evidence suggests neural sequences in the striatum play a central role in timing, the mechanisms underlying the generation of neural sequences remains elusive. Critically, it is not known whether neural sequences are actively generated within the striatum or are “driven” by neural sequences present in corticostriatal inputs. We propose to address these major gaps in understanding with a combination of innovative experimental and computational approaches. Our key hypotheses are that: 1) neural sequences in the striatum provide a flexible dynamical regime that allows for temporal scaling, i.e., speeding-up or slowing-down of motor responses, 2) cortical input shapes neural sequence formation in the striatum, 3) local inhibitory circuits serve to refine the quality of these sequences in the striatum, and 4) neural dynamics encoding time are widely distributed throughout the brain but are more accurate in certain areas such as the striatum. Our project is anchored in a two-interval timing task in which mice learn to associate two cues with different reward delays, and has three major aims. Guided by large-scale neural recordings in multiple brain areas we will first develop cortical and striatal recurrent neural network models with the goal of understanding which circuit motifs are best suited to generate neural sequences, and determining which models best capture the experimentally observed activity patterns. Second, we will integrate neural recordings and optogenetic perturbations, together with computational approaches, to determine whether neural sequences in the striatum are driven by cortical input and refined by local inhibition, or in contrast actively generated within the striatum. Third, we will carry out a high-throughput electrophysiological survey of neural activity in multiple brain areas, to identify which areas contain the most accurate temporal codes as well as the potential computational tradeoffs between different codes. RELEVANCE (See instructions): By integrating advanced computational and experimental approaches, this collaborative project will provide fundamentally new insights about how the mammalian brain is able to predict when external events will occur, enabling animals to produce appropriately timed movements that are critical in daily life. This work will reveal which brain circuits are most strongly implicated in timing, which is often impaired in neurological disorders such as Parkinson’s and Huntington’s disease.

预测何时发生外部事件的能力，例如预期捕食者或 食物的可用性对于生存至关重要。融合计算和实验工作表明 神经元活动的动态变化模式，包括神经元序列，临时预测的基础 和临时处理。越来越清楚的是，时间和临时预测高度分布 但是，计算几乎没有努力系统地对比并理解 在不同的大脑区域编码时间之间的计算权衡。此外， 融合的证据表明，纹状体中的神经序列在时机中起着核心作用， 神经序列产生的机制仍然难以捉摸。至关重要的是，这是不知道的 是在纹状体内积极生成神经元序列还是由神经元序列“驱动” 存在于皮质纹状体输入中。我们建议通过 创新实验和计算方法的组合。我们的主要假设是：1） 纹状体中的神经序列提供了一种灵活的动态状态，可以进行临时缩放，即 运动反应的加速或减速，2）皮质输入形状形状形状中性序列形成 纹状体，3）局部抑制电路可完善这些序列中这些序列的质量，并且 4）编码时间的神经动力学广泛分布在整个大脑中，但更准确 某些区域，例如纹状体。我们的项目锚定在一个两间插座的时序任务中 学会将两个线索与不同的奖励延迟相关联，并具有三个主要目标。由大规模指导 多个大脑区域的神经记录我们将首先发展皮质和纹状体复发性神经网络 模型的目标是了解哪些电路基序最适合生成神经元序列， 并确定哪些模型最能捕获实验观察到的活性模式。第二，我们会的 整合的神经记录和光遗传学扰动以及计算方法 确定纹状体中的中性序列是否由皮质输入驱动，并通过局部进行完善 抑制作用，或在纹状体内积极产生的相反。第三，我们将进行高通量 多个大脑区域神经活动的电生理调查，以确定哪个区域包含 最准确的临时代码以及不同代码之间的潜在计算权衡。 相关性（请参阅说明）： 通过整合高级计算和实验方法，这个协作项目将 从根本上提供有关哺乳动物大脑如何能够预测何时外部的新见解 将发生事件，使动物能够产生适当的定时运动，这在日常生活中至关重要。 这项工作将揭示哪些脑电路在时机中最严重受损，这通常会受到损害 帕金森氏病和亨廷顿氏病等神经系统疾病。