Studying Memory Retrieval at the Dynamic Neural Network Level

研究动态神经网络级别的记忆检索

• 批准号: 8863829
• 负责人: Ziv Williams
• 金额: $ 36.96万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8863829
• 关键词: Algorithms; Alzheimer' s Disease; Animal Model; Animals; Area; Behavior Disorders; Behavioral; Biological Neural Networks; Code; Computational Technique; Cues; Data; Degenerative Disorder; Electrodes; Genes; Higher Order Chromatin Structure; Individual; Inferior; Ising model; Lateral; Learning; Linear Models; Macaca mulatta; Memory; Methodology; Methods; Microelectrodes; Modeling; Nature; Neurodegenerative Disorders; Neurons; Neurosciences; Parkinson Disease; Pattern; Population; Primates; Principal Investigator; Process; Research; Retrieval; Stimulus; Structure; Techniques; Testing; Theoretical Studies; Time; Training; Trees; Up-Regulation; Visual; Work; awake; base; cognitive function; cognitive process; developmental disease; information model; innovation; insight; memory process; memory recall; memory retrieval; millisecond; novel; optogenetics; public health relevance; relating to nervous system; theories; way finding

 DESCRIPTION (provided by applicant): A fundamental question in neuroscience is how memories are represented by the collective pattern of spiking within neural populations. A related question is whether the same collective pattern used to represent memories during learning are "reactivated" during later recall, or if the nature of the population code is vitally different. While prior theoretical work and more recent work in animal models have provided valuable initial insights into how memories are likely represented, the neural process by which memories are actually retrieved at the network-wide level, what exact aspects of the first-, second- and higher-order network structure are informative of the retrieved memories, and by what rapid sub-second dynamic do such informative patterns evolve within individual trials remain fundamentally unknown. This limited understanding is particularly true of hetero-associative memory processes such as cued recollection in which the items being recalled are both absent and completely unique from the presented items used to cue their retrieval. Here, we aim to systematically define, for the first time, the combined network-level processes that underlie these basic forms of auto-associative (recognition) and hetero-associative (recollection) memory. Towards these ends, we will use the shared expertise of the two principal investigators to perform simultaneous multi-electrode recordings from frontal and temporal cortical populations in Rhesus macaques; devise and test novel analysis methodologies that can both reliably infer the collective first-, second- and higher-order functional network structures from stochastic spiking data; track their rapid sub-second dynamics within individual trials; identify which structures are informative of the memories being recalled; determine when and to what extent spiking network structures observed during learning reactivate during recall within individual-day sessions; and, perhaps most importantly, determine whether pattern reactivation, at the spiking network-level, is causatively related to recall accuracy at the behavioral level. Th present proposal will allow us to directly test, for the first time, a number of central hypotheses on memory processing using a novel set of technical and methodological innovations that will have broad practical implications to the study of memory-related developmental, behavioral and neurodegenerative disorders.

 描述（由申请人提供）：神经科学中的一个基本问题是如何通过神经群体内的集体尖峰模式来表示记忆，一个相关的问题是用于表示学习期间记忆的相同集体模式是否在以后的回忆中被“重新激活”。或者，如果群体密码的性质截然不同，虽然先前的理论工作和最近的动物模型工作已经为记忆如何表示提供了有价值的初步见解，但在网络范围内实际检索记忆的神经过程。 ，具体是哪些方面一阶、二阶和高阶网络结构为检索到的记忆提供信息，并且这些信息模式在个体试验中通过何种快速的亚秒动态演化仍然基本上未知，这种有限的理解对于异质联想记忆尤其如此。诸如提示回忆之类的过程，其中被回忆的项目既不存在，又与用于提示其检索的呈现项目完全不同。在这里，我们的目标是首次系统地定义构成这些基本基础的组合网络级过程。自动联想的形式为了实现这些目标，我们将利用两位主要研究人员的共同专业知识，对恒河猴的额叶和颞叶皮层群体进行同步多电极记录，设计并测试新的分析方法。可以从随机尖峰数据中可靠地推断集体一阶、二阶和高阶功能网络结构；跟踪单个试验中的快速亚秒级动态；识别哪些结构可以提供信息；回忆被回忆的记忆；确定在学习过程中观察到的尖峰网络结构何时以及在何种程度上在每天的回忆过程中重新激活；并且，也许最重要的是，确定尖峰网络级别的模式重新激活是否与回忆准确性有关；目前的提案将使我们能够首次直接测试一些中心假设。 使用一套新颖的技术和方法创新来研究记忆处理，这将对记忆相关的发育、行为和神经退行性疾病的研究产生广泛的实际影响。