Project 4: Neuronal Interactions
Project 4: Neuronal Interactions
批准号:
9983182
负责人:
Hyunjune SEBASTIAN SEUNG
金额:
$37.34万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-28 至 2022-07-31
关键词:
AnatomyAnteriorAreaArtificial IntelligenceAutomationBehaviorBiological AssayBiophysicsBrainBrain regionCalciumCodeCuesDataDecision MakingElectron MicroscopyElementsExhibitsFundingGoalsGrantImageInvestigationLaboratoriesMindModelingNeocortexNeuronsOpticsPhysiologicalProcessPropertyRampResearch PersonnelResearch Project GrantsResolutionRoleShort-Term MemorySynapsesTechniquesTechnologyTestingTimeWorkbasecognitive abilitycognitive processcomputerized data processingconnectomecostdesignexperimental studyinnovationinterestmillimeterneural circuitneural modelneuromechanismoptogeneticsreconstructionrelating to nervous systemresponsevirtual
中文摘要
项目摘要:项目4,神经元相互作用的解剖学和生理学分析
工作记忆是一种在脑海中暂时记住多个信息以进行操作的能力,它几乎是所有认知能力的核心。这个多组分的研究项目旨在全面剖析这种能力在多个大脑区域的神经回路机制。总体目标是构建一个跨多个大脑区域的工作记忆和决策的神经电路模型,受神经活动、失活结果和连接性的限制。该模型已经受到了初步数据的限制,这些数据显示了证据积累任务中后部皮质神经元中特定于选择的活动序列。这种建模将生成预测,这将通过几种技术进行测试。第一种方法将结合钙成像和连续切片电子显微镜,将神经编码特性与解剖连接进行比较。这一目标将受益于承德实验室最近在人工智能自动化方面取得的巨大进步,这将使寻找毫米级皮质体积的连接成为可能。第二种方法将结合钙成像和细胞分辨率的光遗传微扰,将神经编码与生理连接进行比较。这一目标将利用Tank实验室最近在全光学连通性分析方面的工作。第三种方法是通过在一个区域的光发生失活期间对另一个区域进行钙成像来研究皮质区域之间的相互作用的作用。这个项目的大部分内容都集中在脾后皮质上,这是特别令人感兴趣的,因为在这部分皮质中,特定选择的活动序列比其他皮质区域更具线性。此外,脾后皮质在时间上的特异性失活会导致模仿删除传入证据的反应的行为。前两种方法将检验示例预测,即潜在活动序列的局部电路将呈现顺序连接,即从较早的神经元到较晚的神经元的连接将比从较晚的神经元到较早的神经元的连接更强或更频繁。第三种方法将测试第二个示例预测,即皮质前M2区域的失活将导致后皮质的非特异性兴奋减少,并改变序列时序。重要的是,这些示例预测并不是全面的。相反,这些技术的普遍性将使研究人员能够测试从神经电路模型中出现的各种预测,这些预测将根据这些实验和项目的其他组成部分的数据生成和改进。
英文摘要
Project Summary: Project 4, Anatomical and Physiological Assays of Neuronal Interactions
Working memory, the ability to temporarily hold multiple pieces of information in mind for manipulation, is central to virtually all cognitive abilities. This multi-component research project aims to comprehensively dissect the neural circuit mechanisms of this ability across multiple brain areas. The overall goal is to construct a neural circuit model of working memory and decisionmaking across multiple brain regions, constrained by neural activity, inactivation results, and connectivity. The model is already constrained by preliminary data showing choice-specific activity sequences in posterior cortical neurons in an evidence-accumulation task. This modeling will generate predictions, which will be tested by several techniques. The first approach will compare neural coding properties with anatomical connectivity by combining calcium imaging with serial-section electron microscopy. This aim will benefit from the Seung laboratory’s dramatic recent progress in automation by artificial intelligence, which will make it feasible to find connectomes of millimeterscale cortical volumes. The second approach will compare neural coding with physiological connectivity by combining calcium imaging with optogenetic perturbation at cellular resolution. This aim will leverage the Tank laboratory’s recent work on alloptical assays of connectivity. The third approach will be to study the role of interactions between cortical areas by performing calcium imaging in one area during optogenetic inactivation of another area. Much of this project focuses on retrosplenial cortex, which is of special interest because choicespecific activity sequences are more linear in this part of cortex than in other cortical areas. Furthermore, temporally specific inactivation of retrosplenial cortex causes behavior that mimics the response to deletion of incoming evidence. The first two approaches will test the example prediction that the local circuitry underlying activity sequences will exhibit sequential connectivity, that is, that connections from earlier to later neurons will be stronger or more frequent than those from later to earlier neurons. The third approach will test a second example prediction, that inactivation of the anterior M2 region of cortex will lead to reduction in nonspecific excitation in posterior cortices and alter sequence timing. Importantly, these example predictions are not intended to be comprehensive. Instead the generality of these techniques will enable researchers to test a wide variety of predictions that emerge from the neural circuit models that will be generated and refined based on data from these experiments and other components of the project.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Structure and Function of a Cubic Millimeter of Cortex: Crowdsourcing for Proofreading and Discovery
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批准号:10025901
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项目类别:
-
资助金额:$635.05万
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财政年份:2020
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
Data Science Core
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批准号:10247579
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项目类别:
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资助金额:$45.41万
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财政年份:2017
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
Project 4: Neuronal Interactions
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批准号:10247571
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项目类别:
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资助金额:$36.45万
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财政年份:2017
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
Data Science Core
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批准号:9983197
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项目类别:
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资助金额:$45.41万
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财政年份:2017
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
Retinal connectome: mobile game and crowdsourcing algorithms for EyeWire II
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批准号:9330810
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项目类别:
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资助金额:$32.09万
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财政年份:2016
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
Retinal connectome: mobile game and crowdsourcing algorithms for EyeWire II
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批准号:9076876
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项目类别:
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资助金额:$32.09万
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财政年份:2016
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
Plasticity of the Human Oculomotor Integrator through Real-Time Visual Feedback
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批准号:7017281
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项目类别:
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资助金额:$8.24万
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财政年份:2006
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
MECHANISMS OF PERSISTENT NEURAL ACTIVITY
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批准号:6528606
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项目类别:
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资助金额:$27.3万
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财政年份:2000
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
MECHANISMS OF PERSISTENT NEURAL ACTIVITY
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批准号:6656355
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项目类别:
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资助金额:$29.76万
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财政年份:2000
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
MECHANISMS OF PERSISTENT NEURAL ACTIVITY
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批准号:6796155
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项目类别:
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资助金额:$30.66万
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财政年份:2000
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
MECHANISMS OF PERSISTENT NEURAL ACTIVITY
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批准号:6195362
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项目类别:
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资助金额:$29.94万
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财政年份:2000
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
MECHANISMS OF PERSISTENT NEURAL ACTIVITY
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批准号:6392680
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项目类别:
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资助金额:$30.71万
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财政年份:2000
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
Project 4: Neuronal Interactions
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批准号:9444133
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项目类别:
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资助金额:$36.45万
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财政年份:--
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
Data Science Core
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批准号:9444126
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项目类别:
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资助金额:$45.41万
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财政年份:--
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负责人:Hyunjune SEBASTIAN SEUNG
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依托单位:
海外基金