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: 10659138
• 负责人: Zachary Samuel Pitkow
• 金额: $ 39.46万
• 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/10659138
• 关键词: Animals; Area; Artificial Intelligence; Behavior; Behavior Control; Behavioral; Belief; Brain; Brain region; Code; Cognition; Cognitive; Complex; Data; Dimensions; Electrophysiology (science); Environment; Ethology; Fireflies; Foundations; Goals; Human; Incentives; Instruction; Juice; Learning; Location; Macaca; Measurable; Measurement; Memory; Methods; Modeling; Monkeys; Motor output; Neurons; Neurosciences; Nonlinear Dynamics; Output; Parietal; 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; invention; neural; neurophysiology; novel; preference; 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 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 不能被实验者直接测量的功能(“思想”)。此次事件的调查人员 目前的建议发明了一种新的方法来模拟动物的思维，通过结合可解释的 自然主义任务的人工智能(XAI)认知模型和动物的测量 感觉输入和行为输出。这个模型被称为逆向理性控制(IRC)，它推断出 内部模型假设，在这些假设下，动物的行为将是最佳的。然后，它提供估计 与这个内部模型一致的关于世界的主观信念的时间序列。这些 估计为评估与任务相关的降维框架提供目标 神经群体活动中的计算动力学。调查人员建议使用这些分析 找到实现这些认知过程的神经表示和转换的工具。他们 将把这一点应用于他们开发的一项复杂的、自然主义的任务：在虚拟现实中捕捉萤火虫。这个 他们成功训练来执行这项任务的猴子显然权衡了不确定性，形成了 预测和长期战略，并应用非线性动力学-所有计算都是 大脑功能的基础。研究人员首先提出将他们的方法应用于分析现有的 行为数据和神经记录收集在这项任务的一个简单版本中，目标是一只萤火虫。 然后，他们将收集关于这项任务的多萤火虫版本的新数据，这激励动物 并实施更长期的计划。为了分析这些数据，调查人员将概括他们的方法 允许他们了解动物选择了哪些压缩表示作为基础 他们的策略。这些结果将被用于形成关于神经计算的预测， 使用本项目期间从多个脑区收集的电生理数据进行测试。这个 这项研究的结果将解释执行复杂的战略性导航任务所需的计算 并将展示一种理解自然主义大脑的新范式 计算。 相关性(请参阅说明)： 这个项目将揭示构成空间基础的灵长类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如何 为了理解复杂的、自然的大脑计算可以应用于各种任务，解释说 无论是适应性行为还是病理性结构化行为。这将为理解 而改善会扰乱人类的认知功能。