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.

大脑皮层电路执行计算以基于不同的 感官输入。这些计算对维持动物的健康和长期- 长期生存。这种类型的计算的一个例子是知觉决策任务 在这种情况下，动物必须权衡感官证据，才能选择会引起奖励的行为。 经典的决策电路模型只关注循环的影响 兴奋，将抑制性神经元视为兴奋性之间竞争的不可知性促进器 亚群。然而，这种关于抑制性神经元的观点与实验结果不一致。 它显示了跨皮质的神经元间调谐和连接的多样性，最近在 决策环境。我建议开发大脑皮层决策回路的新模型 其将兴奋性内亚群之间的连接的选择性参数化 和抑制种群，以了解选择性如何塑造吸引子动态 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，我将建立一个更新的神经回路理论框架 决策行为的机制，更充分地解释了大脑皮质的复杂性 电路结构，更充分地代表了神经元细胞类型的多样性。的作用 促进任务学习的抑制选择性将使用人工神经进行研究。 网络作为代理。最后，单细胞分辨钙活性将从 标记的抑制性细胞类型。这项工作将解决电路结构和单元类型如何形成 决策背后的种群动态以及局部皮质过程如何产生 有意义的行为。