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.

摘要

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。

• DOI: 10.1101/2023.06.01.543054
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Franke,Katrin;Cai,Chenchen;Ponder,Kayla;Fu,Jiakun;Sokoloski,Sacha;Berens,Philipp;Tolias,AndreasS
• 通讯作者: Tolias,AndreasS