Elucidating the neural substrate for value-based decision.

阐明基于价值的决策的神经基础。

• 批准号: 9792263
• 负责人: Bethanny Danskin
• 金额: $ 3.79万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-05 至 2021-09-04
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9792263
• 关键词: Affect; Alzheimer' s Disease; Animal Behavior; Animal Model; Animals; Anterior; Area; Bees; Behavior; Behavioral; Behavioral Model; Brain; Brain region; Calcium; Cells; Complex; Decision Making; Diagnosis; Diagnostic; Disease; Dorsal; Economics; Elements; Exhibits; Flowers; Fluorescence; Frontotemporal Dementia; Gambling; Goals; Human; Huntington Disease; Image; Individual; Interruption; Learning; Measurable; Microscopy; Motor Cortex; Mus; Neurobiology; Neurons; Opsin; Outcome; Parietal Lobe; Parkinson Disease; Parvalbumins; Pathology; Population; Population Research; Probability; Psychological reinforcement; Recording of previous events; Research; Resolution; Rewards; Role; Specificity; Techniques; Testing; Time; Training; Transgenic Mice; Work; awake; base; behavior influence; behavior test; behavioral outcome; calcium indicator; calmodulin-dependent protein kinase II; excitatory neuron; expectation; experience; inhibitory neuron; insight; light microscopy; mouse model; neural circuit; neuropathology; optogenetics; relating to nervous system; theories; tool; two photon microscopy

Project Summary In order to make an optimal value-based decision, an animal must effectively encode and compare the value ascribed to each choice. Wide swaths of cortex are implicated in integrating and representing the variables used to make a decision. However, the specificity and necessity of these areas to decision-making is an open area of study. Evidence suggests distinct, constituent elements of decision-making are encoded differentially across multiple brain areas. Reinforcement learning theory provides a framework to dissect these measurable decision variables from behavioral choice. Given both these decision variables and mesoscopic techniques to record the activity of individual neurons and their populations, the research proposed here aims to characterize the encoding of decision across multiple cortical areas. Using cutting-edge imaging and optogenetic tools, this proposed work will elucidate how ensembles of neurons in the brain compute value from recent experience during a probabilistic reward behavior paradigm. The encoding of value and other decision variables will then be tested with perturbation, interrupting normal activity to test causal function. The results of this work will provide insight to how the brain integrates information across cortical regions in order to reach a decision.

项目摘要 为了做出最佳价值决定，动物必须有效地编码和 比较归因于每个选择的值。宽阔的皮质与整合有关 代表用于做出决定的变量。但是，特异性和必要性 这些决策领域是一个开放的研究领域。有证据表明不同的 决策的组成元素在多个大脑区域之间进行了差异化。 强化学习理论提供了一个框架来剖析这些可衡量的决定 行为选择的变量。鉴于这两个决策变量和介观变量 记录单个神经元及其人群活动的技术，研究 这里提出的旨在表征多个皮质区域的决策的编码。 使用尖端的成像和光遗传学工具，这项提出的工作将阐明如何 大脑中神经元的集合计算出概率期间的最新经验的价值 奖励行为范式。然后，价值和其他决策变量的编码将是 用扰动测试，中断正常活动以测试因果功能。结果的结果 工作将提供有关大脑如何按顺序整合皮质区域信息的洞察力 做出决定。