Using perceptual decision-making to understand the role of selective inhibitory activity in cortical computation

使用感知决策来理解选择性抑制活动在皮质计算中的作用

• 批准号: 10164614
• 负责人: James Patrick Roach
• 金额: $ 7.14万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10164614
• 关键词: Address; Affect; Animals; Behavior; Biological; Brain; Calcium; Cells; Complex; Data; Data Set; Decision Making; Dimensions; Evaluation; Evolution; Excitatory Synapse; Exhibits; Gene Expression; Health; Image; Individual; Inhibitory Synapse; Interneurons; Knowledge; Label; Learning; Measures; Modality; Modeling; Morphology; Mus; Neurons; Performance; Population; Population Dynamics; Population Heterogeneity; Process; Property; Proxy; Recurrence; Resolution; Rewards; Role; Sensory; Shapes; Specificity; Stream; Structure; Synapses; Testing; Theoretical model; Time; Training; Update; Work; artificial neural network; base; cell type; cognitive process; excitatory neuron; experimental study; inhibitory neuron; insight; network models; neural circuit; relating to nervous system; sensory input; statistics; two-photon

Cortical circuits perform computations to generate appropriate behaviors based upon diverse sensory inputs. These computations are central to an animal maintaining its health and long- term survival. An example of this type of computation are perceptual decision-making tasks where an animal must weigh sensory evidence to choose a behavior which will elicit a reward. The classical circuit models of decision-making focus solely on the effects of recurrent excitation, treating inhibitory neurons as agnostic facilitators of competition between excitatory subpopulations. However, this view of inhibitory neurons is at odds with experiment results which show a diversity of interneuron tuning and connectivity across the cortex, recently in the decision-making context. I propose to develop new models of cortical decision-making circuits which parameterizes selectivity of connections between subpopulations within the excitatory and inhibitory populations to understand how selectivity shapes the attractor dynamics underlying decision-making and how these dynamics represent animal choice. Based on the analysis of these models, I will establish an updated theoretical framework for the neural circuit mechanisms of decision-making behaviors which more fully account the intricacies of cortical circuit structure and more fully represent the diversity of neuronal cell-types. The role of inhibitory selectivity in facilitating task learning will be investigated using artificial neural networks as a proxy. Finally, single cell resolution calcium activity will be measured from a labeled inhibitory cell-type. This work will address how circuit structure and cell-type shape population dynamics underlying decision-making and how local cortical processes generate meaningful behaviors.

皮质电路执行计算以基于多样的方式产生适当的行为 感觉输入。这些计算对于维持健康和长期健康的动物至关重要 术语生存。此类计算的一个示例是感知决策任务 动物必须权衡感官证据才能选择一种将引起奖励的行为。 决策的经典电路模型仅关注复发的影响 激发，将抑制性神经元视为兴奋性竞争的不可知论者 亚群。但是，这种抑制神经元的观点与实验结果不一致 最近在皮层中显示了多种多数中间的调整和连通性，最近在 决策背景。我建议开发新的皮质决策电路模型 在兴奋性中，该子群之间的连接选择性参数 和抑制性种群，以了解选择性如何塑造吸引力动力学 潜在的决策以及这些动态如何代表动物的选择。基于 分析这些模型，我将建立一个神经回路的更新理论框架 决策行为的机制更充分说明皮质的复杂性 电路结构，更充分地表示神经元细胞类型的多样性。的作用 将使用人工神经来研究促进任务学习的抑制选择性 网络作为代理。最后，将从A测量单细胞分辨率的钙活性 标记为抑制性细胞类型。这项工作将解决电路结构和电池类型的形状 决策的基本人口动态以及本地皮质过程如何产生 有意义的行为。