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及其执行控制的细胞和电路机制。人类和动物的灵活行为依赖于大脑随时间积累信息、深思熟虑选择、抑制强势反应和选择有目的行动的能力。众所周知，额叶和顶叶皮层对决策至关重要，但这一复杂认知网络的运作在机制层面上仍然知之甚少。我们认为，在决策过程中，感官信息或计划行动的积累是由强循环回路的神经活动实例化的，这种神经活动可以被概念化为吸引子网络。此外，时间积分过程不是固定的，而是可以随时调整以优化行为。我们将与实验学家密切合作，使用基于神经生理学的尖峰网络模型来检验这一假设。我们的模型将针对行为和生理数据（单细胞和局部场电位）进行定量测试，这些数据来自于在动眼力决策任务中行为的猴子。模型预测将通过实验加以验证。人类和猴子的动眼肌系统的结构是相似的，因此我们在工作中获得的知识将可能有助于我们理解人类的决策。此应用程序有四个特定目标。在目标1中，我们将分析皮层回路中随机但相关的混响神经动力学，这是感知证据缓慢时间整合和感知决策反应时间变异性的基础。目的2将研究感觉和运动过程之间的相互作用，以及顶叶-额叶回路中动作的抑制性控制。在Aim 3中，我们将研究决策如何依赖于选择选项的数量及其相似性，以及模拟决策计算如何在包含皮层、基底神经节和上丘的大规模电路模型中导致分类选择的读出。目标4将集中于决策的最优性和灵活性，由奖励依赖的突触可塑性和几个执行控制机制的协调行动实例化。综上所述，这项研究将首次提出顶叶-额叶-基底神经节网络中感觉-运动决策的详细电路模型。