Neural Basis of Causal Inference: Representations, Circuits, and Dynamics

项目摘要 同样的神经活动模式可以对应于世界上的多个事件。信号席卷整个 例如，视网膜可能是由移动的物体产生的，也可能是由动物的自我运动产生的。大脑 通过推断哪些事件最能解释感觉活动，解决了这种模棱两可的问题。这一过程称为因果 推理是所有感觉-运动系统中动作-知觉回路的基础。为了支持自适应动作， 这些计算中涉及的变量的神经表示应该是内部一致的。然而，几乎没有什么 了解这些内部模型是如何产生、发展和交互的。这项建议的重点是神经 物体运动和深度知觉中因果推理的表征、回路和动力学 在自动运动过程中。因为这些变量之间的关系是由物理定义的，而不是任意的 训练有素的联想，因为它们可能由不同的皮质区域代表，该项目将能够 研究大脑皮层之间的联系是如何沟通的，以保持对现实的内部一致看法。这个 总体假设是，因果推理涉及顶叶和/或前额叶皮质的计算，并且 产生的信号被反馈到感觉区域，以更新任务相关变量的神经表示。 项目A将使用贝叶斯建模来开发研究因果推理的理论框架 然后将该方法与实时理性控制理论相结合进行建模 连续、动态的任务。这些模型将用于拟合行为数据并生成量化数据 与项目B和C中的行为和神经反应进行比较的预测使用基于试验的任务 猴子，项目B将询问因果推理如何调节流分析的神经关联(在 背景运动影响对物体运动的感知)，将考察感官表征如何 更新了关于物体运动的因果推断，并将使用化学和光遗传失活来识别 这种感觉表征更新所必需的特定神经通路。在自然主义中， 连续导航任务，项目C将使用类似的记录和神经操作方法来 检查猴子因果推理的神经动力学，并将映射神经动力学的相互关系 在高密度神经记录中对整个小鼠大脑进行因果推断。数据科学核心将 将存储和共享数据的程序正式化，并开发标准的数据处理管道，而 行政核心将在团队之间进行协调，并管理内部和外部咨询委员会。 这些全面的研究工作有望在单一的 神经元和神经细胞群体，并决定由此产生的关于世界状态的信念是如何 从决策区域传播回大脑的感觉区域。成功完成这项工作 这项工作将阐明反馈投影和神经编码在大脑感觉区域的功能作用， 将这个领域推向自然主义的持续行为，并帮助关闭感知和行动之间的循环。

Project Summary The same pattern of neural activity can correspond to multiple events in the world. Signals sweeping across the retina, for instance, might be generated by a moving object or by the animal's self-motion. The brain resolves this ambiguity by inferring what events best explain sensory activity. This process, called causal inference, is a foundation of action-perception loops in all sensory-motor systems. To support adaptive action, neural representations of variables involved in these computations should be internally consistent. Yet little is known about how such internal models arise, evolve, and interact. This proposal focuses on the neural representations, circuits, and dynamics underlying causal inference in perception of object motion and depth during self-motion. Because the relationships among these variables are defined by physics, not arbitrary trained associations, and because they are likely represented by different cortical areas, the project will be able to study how intercortical connections communicate to maintain an internally consistent view of reality. The overall hypothesis is that causal inference involves computations in parietal and/or prefrontal cortex, and the resulting signals are fed back to sensory areas to update neural representations of task-related variables. Project A will use Bayesian modeling to develop the theoretical framework for studying causal inference in traditional trial-based tasks, and then combine this approach with real-time rational control theory to model continuous, dynamic tasks. These models will be used to fit behavioral data and generate quantitative predictions to compare with behavioral and neural responses in Projects B and C. Using trial-based tasks in monkeys, Project B will ask how causal inference modulates neural correlates of flow parsing (in which background motion influences perception of object motion), will examine how sensory representations are updated by causal inference about object motion, and will use chemical and optogenetic inactivation to identify the specific neural pathways that are necessary for such updating of sensory representations. In naturalistic, continuous navigation tasks, Project C will use similar recording and neural manipulation approaches to examine the neural dynamics of causal inference in monkeys, and will map neural correlates of dynamic causal inference across the entire mouse brain in high-density neural recordings. The Data Science Core will formalize procedures for storing and sharing data, and develop a standard data-processing pipeline, while the Administrative Core will coordinate among the team and manage internal and external advisory committees. These comprehensive research efforts are expected to identify direct correlates of causal inference in single neurons and neural populations and determine how the resulting beliefs about states of the world are propagated from decision-making regions back to sensory regions of the brain. Successful completion of this work will illuminate the functional roles of feedback projections and neural coding in sensory areas of the brain, move the field toward naturalistic continuous behavior, and help close the loop between perception and action.