Multiplexing working memory and timing: Encoding retrospective and prospective information in transient neural trajectories.

复用工作记忆和计时：在瞬态神经轨迹中编码回顾性和前瞻性信息。

• 批准号: 10709838
• 负责人: DEAN V BUONOMANO
• 金额: $ 62.04万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10709838
• 关键词: Multiplexing; working; memory; timing; Encoding

Abstract A general principle of brain function is the ability to store information about the past to better predict and prepare for the future. Working memory and timing are two computational features that evolved to allow the brain to use recent information about the past to accomplish short-term goals. Working memory refers to the ability to transiently store information in a flexible manner, while timing refers to the ability to generate well timed motor responses, modulate attention in time, and predict when external events will occur. To date working memory and timing have primarily been treated as independent processes. Here we propose that because the brain seeks to use information about the past to predict the future, that working memory and timing are often multiplexed. Specifically, that the neural patterns of activity recorded during the delay period of many working memory tasks encodes both retrospective information about the past, as well as prospective predictions about the future. To test this hypothesis, we have developed novel variant of the delayed- nonmatch-to-sample task, in which the first cue predicts the duration of the delay, that is, how long an item must be held in working memory. This task will allow us to determine if network level population responses encode both retrospective information about the past and prospective information about delay duration. Preliminary results from a supervised recurrent neural network model predict that the temporal structure of the neural patterns of activity elicited by both cues will be different. This prediction will be tested using large scale Ca2+-imaging to characterize the spatiotemporal patterns of activity in brain areas associated with working memory and timing. Additionally, optogenetic perturbation experiments and longitudinal characterization of the emergence of neural patterns of activity will be performed. These experiments, will in turn, be used to ground computational studies aimed at understanding the neuronal and circuit level learning rules that underlie the emergence of patterns that encode working memory and time.

抽象的 大脑功能的一般原则是能够存储有关过去的信息的能力，以更好地预测和准备 未来。工作记忆和时机是两个计算功能，可以使大脑使用最近 有关过去的信息以实现短期目标。工作记忆是指瞬时存储的能力 以灵活的方式进行信息，而定时是指产生定时电机响应的能力，调整 及时注意，并预测何时发生外部事件。迄今为止的工作记忆和时机主要是 被视为独立过程。在这里我们建议，因为大脑试图使用有关过去的信息 预测未来，工作记忆和时机通常是多重的。具体而言，活动的神经模式 在许多工作记忆任务的延迟期间记录，编码两个回顾性信息 过去，以及对未来的前瞻性预测。为了检验这一假设，我们开发了 延迟 - 非匹配样本任务，其中第一个提示可以预测延迟的持续时间，也就是说，项目多长时间 必须保存在工作记忆中。此任务将使我们能够确定网络级别的人口响应是否编码 有关过去和有关延迟持续时间的前瞻性信息的回顾性信息。来自 监督的复发性神经网络模型预测，活动的神经模式的时间结构引起了 通过这两个提示将有所不同。该预测将使用大型Ca2+成像进行测试以表征 与工作记忆和时机相关的大脑区域活动的时空模式。另外，光遗传学 扰动实验和活动神经模式出现的纵向表征将是 执行。这些实验又将用于基础计算研究，以了解 神经元和电路级别的学习规则是编码工作记忆和时间的模式出现的基础。