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.

摘要 大脑功能的一般原理是存储有关过去的信息以更好地预测和准备未来的能力。 未来工作记忆和计时是两个计算特征，它们的进化使大脑能够使用最近的信息。 关于过去的信息，以实现短期目标。工作记忆指的是瞬时存储 信息以灵活的方式，而定时是指能够产生良好的定时运动反应，调节 及时注意，并预测外部事件何时发生。到目前为止，工作记忆和计时主要是 被视为独立的过程。在这里，我们提出，因为大脑寻求使用有关过去的信息， 预测未来，工作记忆和时间往往是多重的。具体来说，神经活动模式 在许多工作记忆任务的延迟期间记录的信息编码了关于 过去，以及对未来的预测。为了验证这一假设，我们开发了一种新的变体， 延迟-样本不匹配任务，其中第一条线索预测延迟的持续时间，即项目 必须保存在工作记忆中这项任务将使我们能够确定网络水平的群体反应是否编码了 关于过去的回顾性信息和关于延迟持续时间的前瞻性信息。A的初步结果 有监督的递归神经网络模型预测了神经活动模式的时间结构， 这两种线索将是不同的。将使用大规模Ca 2+成像来表征 与工作记忆和计时相关的大脑区域的时空活动模式。此外，光遗传学 扰动实验和纵向表征的出现神经模式的活动将是 执行。这些实验，反过来，将被用来地面计算研究，旨在了解 神经元和电路水平的学习规则，这些规则是编码工作记忆和时间的模式出现的基础。