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。

基本信息

批准号：
10841182
负责人：
DEAN V BUONOMANO
金额：
$ 2.48万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10841182
关键词：
Area Behavioral Brain Brain region Cues Event Future Goals Image Impairment Learning Methods Motor Neural Network Simulation Neurologic Neurons Pattern Population Process Sampling Short-Term Memory Structure Testing Time Variant attentional modulation computer studies experimental study flexibility neural neural patterning novel optogenetics predictive modeling prospective recurrent neural network response spatiotemporal

Area Behavioral Brain Brain region Cues Event Future Goals Image Impairment Learning Methods Motor Neural Network Simulation Neurologic Neurons Pattern Population Process Sampling Short-Term Memory Structure Testing Time Variant attentional modulation computer studies experimental study flexibility neural neural patterning novel optogenetics predictive modeling prospective recurrent neural network response spatiotemporal

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项目摘要

Contact PD/PI: BUONOMANO, DEAN V 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. Project Summary/Abstract Page 6

联系PD/PI：Buonomano，Dean V 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。项目摘要/摘要页面6