Computational foundations of active visual sensing

主动视觉传感的计算基础

• 批准号: 10431247
• 负责人: Mackenzie Weygandt Mathis
• 金额: $ 478.14万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10431247
• 关键词: 3-Dimensional; Animal Behavior; Animals; Area; Augmented Reality; Behavior; Behavioral; Behavioral Model; Brain; Clutterings; Code; Complex; Cues; Data; Decision Making; Discrimination; Electrophysiology (science); Environment; Eye; Foundations; Future; Genetic; Goals; Head; Image; Intelligence; Life; Limb structure; Machine Learning; Measures; Mediating; Methods; Modeling; Movement; Mus; Neural Network Simulation; Neurosciences; Performance; Physics; Policies; Positioning Attribute; Process; Property; Psyche structure; Published Comment; Publishing; Resolution; Rewards; Risk; Role; Sensory; Stimulus; System; Task Performances; Testing; Time; Training; Uncertainty; Vision; Visual; Visual Cortex; Visual system structure; active vision; base; behavior measurement; behavioral response; brain dysfunction; cell type; cost; deep learning; high dimensionality; in silico; insight; motor control; neural circuit; neural model; novel; object recognition; predictive modeling; relating to nervous system; response; theories; tool; visual control; visual information; visual neuroscience; visual processing; visual stimulus

Abstract Vision is an active process: we move our head and eyes to explore the sensory world. This is particularly important in situations where a stationary view provides limited information, such as when looking for an object that is occluded or obscured, which is common in complex natural scenes. However, our understanding of active vision is limited due to experimental and theoretical challenges, including the difficulty of studying vision in freely moving animals and the lack of formal theoretical frameworks that integrate visual representations with actions. In this team project, we will combine expertise in visual neuroscience, behavior, machine learning, and theory, to determine the behavioral, neural, and computational underpinnings of active sensing. Our approach is based on a new theoretical framework of Bounded Rational Control (BRC), and a behavioral task in which mice perform an object recognition task in the presence of occlusion and image corruptions. To enable active sensing, stimuli in the task are rendered real-time in augmented reality based on the animal's viewpoint. In our first aim, we will develop models of active sensing based on constrained visual representations in BRC. In the second aim, we measure behavioral performance (both correct/incorrect responses and full-body movements) during the task, and in the third aim we will measure neural activity across visual cortical areas during the task. For both Aims 2 and 3, we will fit our models to the corresponding behavioral and neural data, and then perform causal tests of our models by presenting novel stimuli predicted to elicit specific responses from the model. Together, these aims will provide a foundational understanding of active vision in the mouse that will support a subsequent U19 proposal taking advantage of genetic tools to investigate the underlying local and long-range neural circuits.

抽象的 视觉是一个主动的过程：我们移动头部和眼睛来探索感官世界。 在静止视图提供有限信息的情况下很重要，例如在寻找对象时 被遮挡或被遮挡，这在复杂的自然场景中很常见，但是，我们对主动的理解。 由于实验和理论挑战，包括自由研究视觉的困难，视力受到限制 移动的动物以及缺乏将视觉表征与动作结合起来的正式理论框架。 在这个团队项目中，我们将结合视觉神经科学、行为、机器学习和理论方面的专业知识， 确定主动传感的行为、神经和计算基础。 有限理性控制（BRC）的新理论框架，以及小鼠执行的行为任务 存在遮挡和图像损坏的情况下的物体识别任务，以实现主动感知、刺激。 任务中的内容根据动物的视角在增强现实中实时呈现。在我们的第一个目标中，我们将。 在 BRC 中开发基于约束视觉表示的主动传感模型。 测量任务期间的行为表现（正确/错误反应和全身运动）， 在第三个目标中，我们将测量任务期间视觉皮层区域的神经活动。 2和3，我们将模型拟合到相应的行为和神经数据，然后进行因果检验 通过呈现预计会引起模型特定反应的新刺激来对我们的模型进行分析。 目标将提供对鼠标主动视觉的基础了解，这将支持后续的 U19 建议利用遗传工具来研究潜在的局部和远程神经回路。

项目成果

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

小鼠视觉皮层颜色对抗反应类型的不对称分布支持卓越的天空色觉。

• 发表时间: 2023-06-05
• 作者: Franke, Katrin;Cai, Chenchen;Ponder, Kayla;Fu, Jiakun;Sokoloski, Sacha;Berens, Philipp;Tolias, Andreas S
• 通讯作者: Tolias, Andreas S