Neural dynamics underlying spatiotemporal cognitive integration

时空认知整合的神经动力学

• 批准号: 10248436
• 负责人: Gabriel Kreiman
• 金额: $ 42.92万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10248436
• 关键词: Architecture; Automobile Driving; Award; Behavior; Behavioral; Behavioral Mechanisms; Bicycling; Brain; Categories; Cognition; Cognitive; Cognitive Science; Complex; Computer Models; Cues; Data; Data Set; Epilepsy; Eye Movements; Goals; Human; Image; Inferior; Investigation; Knowledge; Label; Left; Light; Link; Location; Measurement; Methods; Mind; Modeling; Monitor; Neurologic; Operative Surgical Procedures; Output; Pattern; Perception; Physiological; Physiology; Play; Prefrontal Cortex; Process; Psychophysics; Resolution; Role; Route; Sensory; Signal Transduction; Source; Stimulus; Stream; Technology; Temporal Lobe; Testing; Time; Validation; Vision; Visual; Visual Cortex; Visual system structure; Work; base; behavior measurement; convolutional neural network; experience; experimental study; millisecond; network architecture; neural circuit; neuromechanism; neurophysiology; object recognition; relating to nervous system; response; sensory input; sensory stimulus; spatial integration; spatiotemporal; theories; visual cognition; visual information

Project Summary Our ability to visually interpret the world around us depends on bottom-up computations that extract relevant information from the sensory inputs but it also depends on our accumulated core knowledge about the world providing top-down signals based on prior experience. The goal of this proposal is to study the mechanisms by which visual information is integrated spatially and temporally to combine bottom-up and top- down knowledge. Towards this goal, we combine behavioral measurements, invasive neurophysiological recordings, and computational models. The behavioral data will provide critical constraints about human integrative abilities, particularly through eye movements and the dynamics of recognition. The invasive neurophysiological data will provide high spatiotemporal resolution of neural activity along the inferior temporal cortex and the interactions with pre-frontal cortex, which is hypothesized to be critical for conveying the type of top-down signals required for recognition. Ultimately, a central goal of our proposal is to formalize our understanding of these integrative process via a quantitative computational model. This computational model should be able to capture the behavioral and physiological results and provide testable predictions. During the current award, we have made significant progress towards elucidating the mechanisms underlying pattern completion whereby the visual system is capable of inferring the identity of objects from partial information. Here we consider a set of images and videos that are “minimal” in the sense that they are recognizable but where any further reduction in the amount of spatial or temporal information renders them unrecognizable. We have strong preliminary evidence that suggests that state-of-the-art purely bottom-up theories of recognition instantiated by deep convolutional networks cannot explain human behavior and physiology. Therefore, these types of stimuli provide an ideal arena to investigate how top-down signals, presumably from pre-frontal cortex, modulate the responses along ventral visual cortex to orchestrate recognition. Understanding the neural mechanisms by which core knowledge is incorporated into sensory processing is arguably one of the greatest challenges in Cognitive Science and may have important implications for many neurological and psychiatric conditions that are characterized by dysfunctional top-down signaling and remain poorly understood.

项目摘要 我们对周围世界的视觉解释能力依赖于自下而上的计算， 从感官输入的相关信息，但它也取决于我们积累的核心知识， 世界提供自上而下的信号基于先前的经验。本提案的目的是研究 视觉信息在空间和时间上被整合的机制，以将自下而上和自上而下的联合收割机结合起来， 下知识。为了实现这一目标，我们将联合收割机行为测量、侵入性神经生理学 记录和计算模型。行为数据将提供关于人类的关键约束 整合能力，特别是通过眼球运动和识别的动态。侵袭 神经生理学数据将提供沿下颞叶沿着的神经活动的高时空分辨率 皮层和与前额叶皮层的相互作用，这被假设为传达的类型是至关重要的 自上而下的识别信号。最终，我们提案的一个中心目标是将我们的 通过定量计算模型来理解这些整合过程。这个计算模型 应该能够捕获行为和生理结果，并提供可测试的预测。期间 目前的裁决，我们已经取得了重大进展，阐明机制的基本模式 视觉系统能够从部分信息中推断出物体的身份。 在这里，我们考虑一组图像和视频，这些图像和视频是"最小的"，从某种意义上说，它们是可识别的，但 其中空间或时间信息量的任何进一步减少使它们无法识别。我们 有强有力的初步证据表明，最先进的纯自下而上的承认理论 深度卷积网络的实例化无法解释人类的行为和生理。因此这些 各种类型的刺激提供了一个理想的竞技场来研究自上而下的信号，可能来自前额叶皮层， 调节沿着腹侧视觉皮层的反应以协调识别。了解神经 核心知识被整合到感觉处理中的机制可以说是最伟大的机制之一。 认知科学的挑战，并可能对许多神经和精神疾病有重要影响。 这些疾病的特征是自上而下的信号传导功能障碍，并且仍然知之甚少。