CRCNS: Integrating sensory and prior information to control behavior

系统神经科学的一个基本目标是描述感觉输入是如何整合的，并引导神经系统的行为。 动物的行为。为了能够整合这些输入，早期的感觉系统已经发展出选择性 对于特定的刺激特征，允许他们使用这些特征作为基础来分析输入。我们的目标是 揭示不同的运动信号是如何被整合以产生运动的全局感知的， 了解这种整合失败的条件。我们的建议反映了一个事实，即自适应 复杂环境中的行为面临着许多挑战，从处理噪声和不确定的视觉， 运动信息，以预测目标轨迹意外事件的未来事件及其与 动态的、杂乱的环境。 我们建议使用动态推理作为一个有效的理论框架，以了解大脑如何 将从自然环境的统计数据中详细阐述的先验知识与不同的 在不同的时间尺度上的信息源，以便提取相关的运动信息， 感觉流和预测未来的事件或行动。平滑追踪系统是对这种现象的一个很好的探索 从目标运动计算到目标轨迹预测的分层动态推断。在 我们可以接触到沿着枕顶到- 在非人类和人类灵长类动物中发现的额叶网络。我们试图揭示一个统一的 经验和理论框架，以捕捉跨不同时间尺度的推理。 相关性（参见说明）： 我们将研究传入的感觉信号如何与先前的经验相互作用，以指导行为，使用 动态推理作为一个理论框架。本研究使用了一种特定的跟踪行为（平滑追踪） 阐明了一个基本问题，即大量的感觉器官的协调活动是如何 神经元在面对不断变化的环境，不确定性， 和噪音，这一过程在精神分裂症等神经系统疾病中被破坏。

A fundamental goal of systems neuroscience is to describe how sensory inputs are integrated and guide an animal's behavior. To be able to integrate these inputs, early sensory systems have developed selectivities for specific stimulus features that allow them to analyze the inputs using these features as basis. We aim to uncover how disparate motion signals are integrated to produce a global percept of motion, and to understand the conditions in which such integration fails. Our proposal reflects the fact that adaptive behaviors in complex environments face numerous challenges, from processing noisy and uncertain visual motion information to predict future events on target trajectory contingencies and its interactions with a dynamic, cluttered environment. We propose to use dynamic inference as an efficient theoretical framework to understand how the brain integrates Prior knowledges elaborated from statistical regularities of natural environments with different sources of information across different time scales in order to extract relevant motion information from the sensory flow and predict future events or actions. The smooth pursuit system is an excellent probe of such hierarchical dynamical inferences from target motion computation to target trajectory prediction. In marmosets, we have access to populations of neurons in pivotal cortical areas along the occipito-parieto- frontal network that have been identified in non-human and human primates. We seek to uncover a unifying empirical and theoretical framework to capture inference across different time scales. RELEVANCE (See instructions): We will examine how incoming sensory signals interact with prior experiences to guide behavior, using dynamic inference as a theoretical framework. This study uses a specific tracking behavior (smooth pursuit) to shed light on the fundamental problem of how the coordinated activity of large populations of sensory neurons is parsed and converted into appropriate behaviors in the face of changing contexts, uncertainty, and noise, a process disrupted in neurological disorders such as schizophrenia.