CRCNS: Neural computations for continuous control in virtual reality foraging

CRCNS：虚拟现实觅食中连续控制的神经计算

• 批准号: 10266181
• 负责人: Zachary Samuel Pitkow
• 金额: $ 39.45万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10266181
• 关键词: Animals; Area; Artificial Intelligence; Behavior; Behavior Control; Behavioral; Belief; Brain; Brain region; Cognition; Cognitive; Complex; Data; Dimensions; Electrophysiology (science); Environment; Fireflies; Foundations; Goals; Human; Incentives; Instruction; Juice; Learning; Location; Macaca; Measurable; Measurement; Memory; Methods; Modeling; Monkeys; Motor output; Neurons; Neurosciences; Nonlinear Dynamics; Output; Parietal; Parietal Lobe; Pathologic; Perception; Population; Prefrontal Cortex; Primates; Process; Research; Research Personnel; Research Proposals; Rewards; Sensory; Series; Strategic Planning; Structure; Testing; Thinking; Time; Training; Uncertainty; Utah; cognitive function; cognitive process; design; improved; insight; neurophysiology; novel; preference; relating to nervous system; sensory input; theories; tool; virtual reality; way finding

Neuroscience has been able to gain major insights by relating measurements of neural activity to the brain’s sensory inputs and motor outputs. Yet most neural activity supports computations and cognitive functions (‘thoughts’) that are not directly measurable by the experimenter. The investigators for the present proposal invented a novel method to model an animal's thoughts by combining eXplainable Artificial Intelligence (XAI) cognitive models for naturalistic tasks with measurements of the animal’s sensory inputs and behavioral outputs. This model, called Inverse Rational Control (IRC), infers the internal model assumptions under which an animal's actions would be optimal. It then provides estimates of time series of subjective beliefs about the world that are consistent with this internal model. These estimates provide targets for a dimensionality reduction framework that assesses task-relevant computational dynamics within neural population activity. The investigators propose to use these analysis tools to find neural representations and transformations that implement these cognitive processes. They will apply this to a complex, naturalistic task that they developed: catching fireflies in virtual reality. The monkeys they successfully trained to perform this task demonstrably weigh uncertainty, develop predictions and long-term strategies, and apply nonlinear dynamics — all computations that are fundamental for brain function. The investigators propose first to apply their method to analyze existing behavioral data and neural recordings collected in a simple version of this task with a single target firefly. They will then collect new data on a multi-firefly version of the task, which incentivizes animals to make and implement longer-term plans. To analyze this data, the investigators will generalize their approach to allow them to learn which compressed representations are selected by the animal as the foundation for their strategies. These results will be used to form predictions about neural computations that will be tested using the electrophysiological data collected from multiple brain regions during this project. The results of this study will explain the computations required to perform a complex, strategic navigation task in the presence of uncertainty, and will demonstrate a new paradigm for understanding naturalistic brain computations. RELEVANCE (See instructions): This project will uncover the neural basis of cognitive processes in the primate brain that underlie spatial navigation, strategic planning, and behavioral control. It will demonstrate how a powerful new paradigm for understanding complex, natural brain computations can apply to a wide variety of tasks, to explain either adaptive or pathologically structured behavior. This will provide crucial guidance for understanding and improving disrupted human cognitive function.

神经科学已经能够通过将神经活动的测量与神经元的活动相关联来获得重要的见解。 大脑的感觉输入和运动输出。然而，大多数神经活动支持计算和认知 这些功能（“思想”）不能被实验者直接测量。调查人员 目前的提议发明了一种新的方法，通过结合可解释的 人工智能（XAI）的自然任务的认知模型与动物的测量 感官输入和行为输出。这个模型被称为逆理性控制（IRC）， 动物行为最优的内部模型假设。然后，它提供估计数， 关于世界的主观信念的时间序列与这个内部模型相一致。这些 估计为评估任务相关性的降维框架提供了目标 神经群体活动中的计算动力学。研究人员建议使用这些分析 寻找实现这些认知过程的神经表征和转换的工具。他们 将把它应用到他们开发的一个复杂的自然主义任务中：在虚拟现实中捕捉萤火虫。的 他们成功训练的猴子能够完成这项任务， 预测和长期战略，并应用非线性动力学-所有计算， 是大脑功能的基础研究人员建议首先应用他们的方法来分析现有的 行为数据和神经记录收集在一个简单的版本，这个任务与一个单一的目标萤火虫。 然后，他们将收集关于多萤火虫版本任务的新数据，该任务激励动物做出 并实施长期计划。为了分析这些数据，研究人员将他们的方法概括为 允许他们学习哪些压缩表示被动物选择作为基础， 他们的战略。这些结果将被用于形成关于神经计算的预测， 使用在这个项目中从多个大脑区域收集的电生理数据进行测试。的 这项研究的结果将解释执行复杂的战略导航任务所需的计算 在不确定性的存在，并将展示一个新的范式，为理解自然主义的大脑 计算。 相关性（参见说明）： 这个项目将揭示灵长类动物大脑中认知过程的神经基础， 导航、战略规划和行为控制。它将展示一个强大的新范式 为了理解复杂的，自然的大脑计算可以应用于各种各样的任务，以解释 适应性或病态结构行为。这将为理解 并改善受损的人类认知功能。