Recurrent Neual Circuit Basis of Time Integration and Decision Making

时间积分和决策的循环神经电路基础

• 批准号: 7369653
• 负责人: XIAO-JING WANG
• 金额: $ 37.14万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7369653
• 关键词: Animals; Basal Ganglia; Behavior; Behavioral; Biological Neural Networks; Brain; Cations; Cells; Chromosome Pairing; Cognitive; Collaborations; Complex; Computer Simulation; Corpus striatum structure; Data; Decision Making; Discrimination; Dopamine; Ensure; Eye; Goals; Human; Knowledge; Laboratories; Lead; Learning; Maintenance; Modeling; Modus; Monkeys; Motion; Motor; Neurons; Numbers; Operative Surgical Procedures; Parietal Lobe; Pattern; Physiological; Pliability; Primates; Process; Property; Ramp; Reaction Time; Recurrence; Research; Rewards; Sensory; Short-Term Memory; Signal Transduction; Speed; Stimulus; Structure; Synapses; Synaptic plasticity; System; Testing; Time; Work; analog; base; executive function; frontal lobe; lateral intraparietal area; network models; neural circuit; oculomotor; relating to nervous system; response; sensory integration; statistics; superior colliculus Corpora quadrigemina

DESCRIPTION (provided by applicant): The long-term goal of our research is to elucidate the cellular and circuit mechanisms of decision making and its executive control. Flexible behavior in humans and animals relies on the brain's ability to accumulate information over time, deliberate about choice options, inhibit prepotent responses, and select purposeful actions. The frontal and parietal cortices are known to be critical to decision making, but the operation of this complex cognitive network is still poorly understood at the mechanistic level. We propose that accumulation of sensory information or planned action in decision making is instantiated by neural activity of strongly recurrent circuits that can be conceptualized as attractor networks. Moreover, the time integration process is not fixed, but can be readily adjusted to optimize behavior. We will test this hypothesis using neurophysiologically-based spiking network models, in close collaboration with experimentalists. Our models will be quantitatively tested against behavioral and physiological data (single-cell and local field potential) collected from behaving monkeys in oculomotor decision tasks. Model predictions will be checked experimentally. The structure of the oculomotor system is similar in humans and monkeys, therefore the knowledge gained in our work will be likely to contribute to our understanding of human decision making. This application has four Specific Aims. In Aim 1 we will analyze stochastic, yet correlated, reverberatory neural dynamics in a cortical circuit that underlies the slow time integration of sensory evidence and the variability of reaction times in perceptual decisions. Aim 2 will investigate the interplay between sensory and motor processes, and inhibitory control of action, in a parieto-frontal circuit. In Aim 3, we will examine how decision making depends on the number of choice alternatives and their similarity, and how analog decision computation leads to the readout of a categorical choice, in a large-scale circuit model encompassing cortex, basal ganglia, and superior colliculus. Aim 4 will be focused on optimality and flexibility of decision making instantiated by reward-dependent synaptic plasticity and the concerted action of several executive control mechanisms. Taken together, the proposed research will advance, for the first time, a detailed circuit model of sensory-motor decisions in the parieto-fronto-basal ganglia network.

描述（由申请人提供）：我们研究的长期目标是阐明决策制定的细胞和电路机制及其执行控制。人类和动物的灵活行为取决于大脑随着时间的推移积累信息的能力，故意选择选择，抑制了预能的反应以及选择有目的的行动。众所周知，额叶和顶叶皮层对于决策至关重要，但是在机械水平上，这种复杂的认知网络的运作仍然很少。我们建议，在决策中积累的感觉信息或计划的行动是通过强烈反复回复的电路的神经活动来实例化的，这些电路可以概念化为吸引者网络。此外，时间集成过程不是固定的，而可以很容易地调整以优化行为。我们将与实验者密切合作，使用基于神经生理学的尖峰网络模型来检验这一假设。我们的模型将针对从眼球决策任务中的行为猴子收集的行为和生理数据（单细胞和局部现场潜力）进行定量测试。模型预测将进行实验检查。在人类和猴子中，动眼系统的结构相似，因此我们工作中获得的知识很可能有助于我们对人类决策的理解。该应用程序具有四个具体目标。在AIM 1中，我们将在皮质回路中分析随机的，但相关的复晶神经动力学，这是感觉证据的缓慢整合和感知决策中反应时间的变化的基础。 AIM 2将研究感觉过程和运动过程之间的相互作用，以及对甲状腺额叶电路中的抑制作用控制。在AIM 3中，我们将研究决策如何取决于选择替代方案的数量及其相似性，以及模拟决策计算如何导致分类选择的读数，在涵盖皮层，基础神经节和上级胶囊的大规模电路模型中。 AIM 4将集中在奖励依赖性突触可塑性和几种执行控制机制的协同作用实例化的决策中的最佳性和灵活性上。综上所述，拟议的研究将首次推进parieto-fronto-basal神经节网络中感觉运动决策的详细电路模型。