CRCNS: Integrating sensory and prior information to control behavior

CRCNS：整合感觉和先验信息来控制行为

• 批准号: 10687117
• 负责人: Nicholas J Priebe
• 金额: $ 27.08万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10687117
• 关键词: Adaptive Behaviors; Affect; Afferent Neurons; Animal Behavior; Area; Behavior; Behavior Control; Brain; Callithrix; Clutterings; Complex; Dimensions; Disparate; Electrophysiology (science); Environment; Event; Eye Movements; Face; Frequencies; Future; Goals; Human; Instruction; Knowledge; Lead; Macaca; Maps; Measures; Motion; Nature; Neurons; Neurosciences; Noise; Phase; Population; Primates; Process; Property; Recording of previous events; Records; Retina; Reverse engineering; Schizophrenia; Sensory; Shapes; Signal Transduction; Smooth Pursuit; Source; Speed; Stimulus; Surface; System; Texture; Time; Uncertainty; Update; Visual; Visual Motion; Visual System; area MT; coping; expectation; experience; eye velocity; nervous system disorder; neural; neural correlate; object motion; perceptual organization; response; sensory input; sensory integration; sensory system; spatiotemporal; statistical learning; two photon microscopy; visual motor

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.

系统神经科学的一个基本目标是描述感官输入如何集成并指导 动物的行为。为了整合这些输入，早期的感觉系统已经开发了选择性 对于特定的刺激特征，使他们可以使用这些特征作为基础分析输入。我们的目标 发现如何整合不同的运动信号以产生对运动的全球感知，并 了解这种集成失败的条件。我们的建议反映了适应性的事实 复杂环境中的行为面临许多挑战，从处理嘈杂和不确定的视觉 运动信息以预测目标轨迹意外事件的未来事件及其与A的相互作用 动态，混乱的环境。 我们建议将动态推断用作有效的理论框架，以了解大脑如何 整合从自然环境的统计规律性详细阐述的知识 跨不同时间尺度的信息来源，以便从 感觉流并预测未来的事件或行动。平稳的追求系统是这样的极好的探究 分层动力学推断从目标运动计算到目标轨迹预测。在 Marmosets，我们可以进入枕皮层 - 帕特托 - 在非人类和人类灵长类动物中已确定的额叶网络。我们试图揭开统一的 经验和理论框架，以捕获不同时间尺度的推论。 相关性（请参阅说明）： 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，并使用 动态推断为理论框架。这项研究使用特定的跟踪行为（平滑追求） 阐明了基本问题，即大量感觉的协调活动如何 面对不断变化的环境，不确定性， 和噪声，这是在精神分裂症等神经系统疾病中破坏的过程。