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: 7928197
• 负责人: XIAO-JING WANG
• 金额: $ 37.24万
• 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/7928197
• 关键词: Animals; Basal Ganglia; Behavior; Behavioral; Biological Neural Networks; Brain; Cells; 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; Parietal Lobe; Pattern; Physiological; 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; flexibility; frontal lobe; lateral intraparietal area; network models; neural circuit; oculomotor; operation; 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着时间的推移积累信息的能力，考虑选择选项，抑制优势反应，并选择有目的的行动。额叶和顶叶皮质对决策至关重要，但这个复杂的认知网络的运作在机制层面上仍然知之甚少。我们建议，在决策过程中的感官信息或计划行动的积累是通过强循环回路的神经活动来实例化的，这种回路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，而是可以容易地调整以优化行为。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测试这一假设。我们的模型将进行定量测试，对行为和生理数据（单细胞和局部场电位）收集行为猴子在眼科决策任务。模型预测将通过实验进行检验。眼神经系统的结构在人类和猴子中是相似的，因此我们在工作中获得的知识可能有助于我们理解人类的决策。这项申请有四个具体目标。在目标1中，我们将分析随机的，但相关的，在皮层回路中的反射神经动力学，其基础是感觉证据的缓慢时间整合和感知决策中反应时间的可变性。目的2将探讨在顶额神经回路中，感觉和运动过程之间的相互作用，以及对动作的抑制控制。在目标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导致分类选择的读出，在一个包含皮层，基底神经节和上级丘的大规模电路模型中。目标4将集中在奖励依赖性突触可塑性和几个执行控制机制的协调行动所体现的决策的最优性和灵活性。综上所述，这项研究将首次提出一个详细的顶额基底神经节网络中感觉运动决策的电路模型。