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Analyses of the Distributed Representation of Associative-Learning in an Identified Circuit Using a Combination of Single-Cell Electrophysiology and Multicellular Voltage-Sensitive Dye Recordings

Analyses of the Distributed Representation of Associative-Learning in an Identified Circuit Using a Combination of Single-Cell Electrophysiology and Multicellular Voltage-Sensitive Dye Recordings
结合单细胞电生理学和多细胞电压敏感染料记录分析已识别电路中联想学习的分布式表示
批准号:
10539225
负责人:
John H Byrne
金额:
$39.0万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
未结题
起止时间:
2018-02-01 至 2027-11-30

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中文摘要
翻译
项目摘要/摘要 尽管在阐明细胞、生物物理和分子方面取得了重大进展 学习和记忆的机制,对助记过程是如何进行的知之甚少 嵌入到神经元网络中。这项提案的总体目标是提供对设计原则的见解 在神经电路的复杂环境中管理记忆的实现。研究将集中于 关于在相对复杂的回路中建立的操作性条件反射(QC)的体外模拟,这是可服从的 到全人群、细胞和生物物理分析。细胞内和细胞外的结合 电生理技术、电压敏感染料(VSD)成像、降维分析以及 计算模型将识别和描述非突触和突触可塑性的基因座。此外, 该项目将研究短期记忆和长期记忆在多大程度上具有可塑性。目标1 将使用细胞内记录技术来检测QC诱导的可塑性。中OC的先前关联性 这一模型系统仅限于增加脑内关键神经元的内在兴奋性或电突触。 巡回赛。我们最近的结果表明,QC也降低了抑制性突触的强度和兴奋性 电路中的一个关键神经元。目标1将检查QC诱导的突触和非突触的其他主要候选者 可塑性,它在调节行为方面起着既定的作用。此外,我们将使用细胞内 检查我们最近的VSD记录显示为QC诱导的电路区域的技术 活动的变化。计算建模将评估基因座单边或协同工作的方式 以调节OC的表型。AIM 2将使用细胞内记录、VSD成像和 降维方法,以扩大对QC诱发塑性的额外位置的搜索 搜索OC的低维“签名”。目标1和目标2的合并结果将提供 对与OC相关的可塑性机制范围的评估在任何系统中都是前所未有的。一个 目标3将解决更重要的问题,它将决定网站在多大程度上短期内 记忆在长期记忆中持续存在,相反,哪些可塑性部位可能是长期记忆所特有的 记忆。本提案将有助于全面了解下列方式 存储器被编码在相对复杂的电路中,阐明了存储器编码的设计原则,以及 为更复杂系统中的类似分析提供指导。
英文摘要
PROJECT SUMMARY/ABSTRACT Although significant advances have been made in elucidating the cellular, biophysical and molecular mechanisms of learning and memory, much less is known about the ways in which mnemonic processes are embedded in neuronal networks. The overall goal of this proposal is to provide insights into the design principles that govern the implementation of memories within the complex environment of a neural circuit. Studies will focus on an established in vitro analogue of operant conditioning (QC) in a relatively complex circuit, which is amenable to population-wide, cellular, and biophysical analyses. A combination of intra- and extracellular electrophysiological techniques, voltage-sensitive dye (VSD) imaging, dimensionality reduction analysis, and computational modeling will identify and characterize loci of non-synaptic and synaptic plasticity. In addition, the project will examine the extent to which plasticity loci are shared between short- and long-term memory. Aim 1 will use intracellular recording techniques to examine loci of QC-induced plasticity. Previous correlates of OC in this model system were restricted to increases in intrinsic excitability or electrical synapses of key neurons in the circuit. Our recent results indicate QC also decreases the strength of an inhibitory synapse and the excitability of a key neuron in the circuit. Aim 1 will examine other prime candidates of QC-induced synaptic and non-synaptic plasticity, which have an established role in mediating the behavior. In addition, we will use intracellular techniques to examine regions of the circuit that our recent VSD recordings have shown to exhibit QC-induced changes in activity. Computational modeling will assess the ways in which loci work unilaterally or synergistically to mediate the OC phenotype. Aim 2 will use a combination of intracellular recordings, VSD imaging, and dimensionality reduction approaches to expand the search for additional sites of QC-induced plasticity and search for low-dimensional 'signatures' of OC. The combined results from Aims 1 and 2 will provide for an assessment of the scope of plasticity mechanisms associated with OC that is unprecedented in any system. A further important question will be addressed by Aim 3, which will determine the extent to which sites for short-term memory persist during long-term memory and, conversely, which sites of plasticity may be unique to long-term memory. The present proposal will help develop a comprehensive understanding of the ways in which memories are encoded in a relatively complex circuit, elucidate design principles of memory encoding, and provide guidance for similar analyses in more complex systems.
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A novel approach to analyzing functional connectomics and combinatorial control in a tractable small-brain closed-loop system
A novel approach to analyzing functional connectomics and combinatorial control in a tractable small-brain closed-loop system
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Modeling the Molecular Networks that Underlie the Formation and Consolidation of Memory
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