CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep
CRCNS:睡眠中海马序列动态的无监督学习
基本信息
- 批准号:10614754
- 负责人:
- 金额:$ 7.06万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-08-15 至 2024-05-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAnimalsBrainCellsCognitionCollaborationsCommunitiesDataElectric StimulationEnvironmentEvaluationEventHippocampusHospitalsHourInformation StorageInstructionLearningMemoryMemory impairmentMethodsModelingNatureNeuronsNeurosciencesNoisePathway interactionsPatternPlayPopulationPublic PolicyReportingRestRoleSchoolsSleepStructureTechniquesTimeTrainingVisitawakedesignexamination questionsexperienceimprovedinformation organizationinterestmarkov modelmemory consolidationmemory processnoveloptogeneticssequence learningsoundtheoriesunsupervised learning
项目摘要
In unit recordings from large populations of neurons, fast compressed sequential firing of neurons during
rest and early sleep have been found to replay patterns first observed in active awake experience. These
remarkable patterns have sparked widespread interest in the scientific community and beyond. Sequence
replay is now considered to play a critical role in the long-term stabilization and storage of mnemonically
important information. However, despite the general acknowledgement of the importance of the sequential
structure, very little is known about the null background against which replay is compared.
Specifically, are apparently 'non-replaying' spike patterns, as seen in late sleep, just simply noise?
Because replay is typically assessed by comparison against a fixed known template, most methods can
only determine whether the resemblance to the template is more than what might be expected from
random spike trains. But these methods cannot appraise whether other patterns remain in the
nonsignificant events. Recently, the Diba and Kemere labs successfully collaborated to address precisely
this issue. We developed methods based on hidden Markov models (HMMs) to uncover temporal
structure in spike trains of neurons in an unsupervised template-free manner. In this proposal, we aim to
further improve these methods and to evaluate the hidden structure of spike trains in hippocampal
neuronal populations during sleep. In our second specific aim, we will use HMMs to determine both
co-active ensemble ("contextual") and temporal patterns ("sequential") structure in hippocampal spike
trains in both pre- and post-task sleep. In the third specific aim, we will probe the essence of sleep replay
further, by exposing animals to multiple novel and familiar maze environments prior to long durations of
sleep. In the fourth specific aim, we will perform closed-loop disruption of neuronal population patterns to
examine the causal interplay and reverberation of these patterns from early to late sleep. In summary, our
proposal is designed to provide strongest characterization to date of the structure of "noise" in replay
events.
RELEVANCE (See instructions):
This study will provide an opening to evaluate the role of sleep in reorganizing information in the brain and
help to identify critical time windows and neuronal activities during sleep which are particularly important
for information storage and stabilization. Our assumptions and deductions about the nature and purpose
of sleep implicitly inform all manner of public policy, from the durations of shifts for hospital and relief
workers, to morning start times of public schools. Understanding the function and mechanisms of sleep H
在大量神经元的单元记录中,快速压缩的神经元连续放电
项目成果
期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Tracing a Path for Memory in the Hippocampus.
- DOI:10.1016/j.neuron.2020.06.034
- 发表时间:2020-07-22
- 期刊:
- 影响因子:16.2
- 作者:Dutta S;Gao S;Chu JP;Kemere C
- 通讯作者:Kemere C
Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations.
不确定性下的觅食遵循边际值定理和环境表示的贝叶斯更新。
- DOI:10.1101/2024.03.30.587253
- 发表时间:2024
- 期刊:
- 影响因子:0
- 作者:Webb,James;Steffan,Paul;Hayden,BenjaminY;Lee,Daeyeol;Kemere,Caleb;McGinley,Matthew
- 通讯作者:McGinley,Matthew
Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding.
用于无监督神经解码的扩展泊松高斯过程潜变量模型。
- DOI:10.1101/2024.03.04.583340
- 发表时间:2024
- 期刊:
- 影响因子:0
- 作者:Luo,DellaDaiyi;Giri,Bapun;Diba,Kamran;Kemere,Caleb
- 通讯作者:Kemere,Caleb
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{{ truncateString('KAMRAN DIBA', 18)}}的其他基金
Div Supp: Daniela del Rio Pulido CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep
Div Supp:Daniela del Rio Pulido CRCNS:睡眠中海马序列动态的无监督学习
- 批准号:
10527115 - 财政年份:2022
- 资助金额:
$ 7.06万 - 项目类别:
CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep
CRCNS:睡眠中海马序列动态的无监督学习
- 批准号:
10542964 - 财政年份:2019
- 资助金额:
$ 7.06万 - 项目类别:
CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep
CRCNS:睡眠中海马序列动态的无监督学习
- 批准号:
10191062 - 财政年份:2019
- 资助金额:
$ 7.06万 - 项目类别:
CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep
CRCNS:睡眠中海马序列动态的无监督学习
- 批准号:
10405544 - 财政年份:2019
- 资助金额:
$ 7.06万 - 项目类别:
CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep
CRCNS:睡眠中海马序列动态的无监督学习
- 批准号:
9916188 - 财政年份:2019
- 资助金额:
$ 7.06万 - 项目类别:
CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep
CRCNS:睡眠中海马序列动态的无监督学习
- 批准号:
10614540 - 财政年份:2019
- 资助金额:
$ 7.06万 - 项目类别:
Enhanced cAMP Signaling Effects on Hippocampal Oscillations and Memory
增强 cAMP 信号对海马振荡和记忆的影响
- 批准号:
9762981 - 财政年份:2018
- 资助金额:
$ 7.06万 - 项目类别:
CRCNS: US-German Proposal: Mechanisms of Sequence Generation in the Hippocampus
CRCNS:美德提案:海马序列生成机制
- 批准号:
9606684 - 财政年份:2017
- 资助金额:
$ 7.06万 - 项目类别:
CRCNS: US-German Proposal: Mechanisms of sequence generation in the hippocampus
CRCNS:美德提案:海马序列生成机制
- 批准号:
9119092 - 财政年份:2015
- 资助金额:
$ 7.06万 - 项目类别:
Optogenetic disruption of the multi-synaptic pathway to CA1 during hippocampal oscillations
海马振荡期间 CA1 多突触通路的光遗传学破坏
- 批准号:
9068352 - 财政年份:2015
- 资助金额:
$ 7.06万 - 项目类别:
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