Computational and Circuit Mechanisms for information transmission in the brain

大脑信息传输的计算和电路机制

• 批准号: 9613104
• 负责人: Uri Tzvi Eden
• 金额: $ 17.03万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9613104
• 关键词: Adaptive Behaviors; Address; Area; Auditory; Behavior; Behavior Control; Behavioral; Brain; Brain region; Cells; Cognitive; Collaborations; Communication; Complex; Computational Technique; Computing Methodologies; Corpus striatum structure; Data; Data Set; Decision Making; Dimensions; Electrodes; Evaluation; Future; Goals; Hearing; Hippocampus (Brain); Interneurons; Laboratories; Learning; Link; Measures; Modeling; Motivation; Motor; Motor Pathways; Motor output; Neurons; Neurosciences; Output; Pattern; Polymers; Population; Process; Research Personnel; Scientist; Sensory; Specificity; Statistical Models; Stream; Structure; System; Techniques; Technology; Testing; Theoretical model; Time; Ventral Striatum; Viral; Waxes; Writing; base; cell type; improved; information framework; information processing; insight; millisecond; motivated behavior; neural circuit; neural model; new technology; novel; operation; optogenetics; public health relevance; relating to nervous system; tool; transmission process; visual motor; Adaptive Behaviors; Address; Area; Auditory; Behavior; Behavior Control; Behavioral; Brain; Brain region; Cells; Cognitive; Collaborations; Communication; Complex; Computational Technique; Computing Methodologies; Corpus striatum structure; Data; Data Set; Decision Making; Dimensions; Electrodes; Evaluation; Future; Goals; Hearing; Hippocampus (Brain); Interneurons; Laboratories; Learning; Link; Measures; Modeling; Motivation; Motor; Motor Pathways; Motor output; Neurons; Neurosciences; Output; Pattern; Polymers; Population; Process; Research Personnel; Scientist; Sensory; Specificity; Statistical Models; Stream; Structure; System; Techniques; Technology; Testing; Theoretical model; Time; Ventral Striatum; Viral; Waxes; Writing; base; cell type; improved; information framework; information processing; insight; millisecond; motivated behavior; neural circuit; neural model; new technology; novel; operation; optogenetics; public health relevance; relating to nervous system; tool; transmission process; visual motor

 DESCRIPTION (provided by applicant): The brain is a massively interconnected network of regions, each of which contains neural circuits that process information related to combinations of sensory, motor and internal variables. Adaptive behavior requires that these regions communicate: sensory and internal information must be evaluated and used to make a decision, which must then be transformed into a motor output. Despite the importance of this question, we know relatively little about the principles of how spiking activity in one region influences activiy in downstream areas, particularly in the context of cognitive operations like decision-making. Here we propose to address this question by focusing on how the ventral striatum (VS), a region critical for motivational control of behavior receives and processes information from two important upstream regions, the orbitofrontal cortex (OFC) and the hippocampus (HP). We have assembled a unique team of scientists with complementary expertise studying the HP (Frank), OFC and VS (Kepecs), using synergistic technologies for large-scale recordings using novel polymer electrodes (Frank/Tolosa) with improved optogenetic identification of projections (Kepecs), and a team of statistical and computational researchers providing complementary analytical expertise in dimensionality reduction (Machens), statistical modeling (Eden/Kramer) and normative models (Ganguli). Our combined expertise will allow us to (1) measure large populations of neurons across the brain regions, (2) identify and (3) manipulate the neurons connecting them in order to (4) test for the first time a range of hypotheses about different modes and circuits for information transmission across regions. Beyond revealing how the OFC, HP and VS communicate during learning and decision-making, our approach will provide new experimental tools and computational methods for systems neuroscience, as well as new insights into the general principles of information transmission across regions.

 描述（由适用提供）：大脑是一个大规模互连的区域网络，每个区域都包含神经回路，这些神经回路处理与感觉，电机和内部变量组合有关的处理信息。自适应行为要求这些区域进行交流：必须评估和内部信息来做出决定，然后必须将其转换为电动机输出。尽管这个问题很重要，但我们对一个地区的峰值活动的原则相对较少，从而影响下游地区的活动，尤其是在决策等认知操作的背景下。在这里，我们建议通过关注腹侧纹状体（VS）是如何从两个重要上游区域（OFC）和海马（HP）的两个重要上游区域（HP）接收和处理信息的关键区域的腹侧纹状体（VS）。我们通过研究HP（Frank），OFC和VS（KEPEC）的互补专业知识组成了一支独特的科学家团队，使用新型聚合物电极（Frank/Tolosa）进行大规模记录的协同技术（Frank/Tolosa），并改善了项目的统计和计算分析统计学分析，统计学分析（KEPECS），并提供了改进建模（Eden/Kramer）和正常模型（Ganguli）。我们的综合专业知识将使我们能够（1）测量整个大脑区域的大量神经元，（2）识别和（3）操纵连接它们的神经元，以便（4）首次测试（4）关于不同模式和电路的一系列假设，以进行跨区域的信息传播。除了揭示OFC，HP和VS在学习和决策过程中如何进行交流外，我们的方法还将为系统神经科学提供新的实验工具和计算方法，以及对跨地区信息传播的一般原理的新见解。