Neural dynamics underlying spatiotemporal cognitive integration

时空认知整合的神经动力学

• 批准号: 10442811
• 负责人: Gabriel Kreiman
• 金额: $ 41.63万
• 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 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10442811
• 关键词: Appearance; Attention; Award; Behavior; Behavioral; Belief; Brain; Clutterings; Cognition; Cognitive; Cognitive Science; Communication; Complex; Computer Graphics; Computer Models; Cues; Data; Data Set; Diet; Electric Stimulation; Eye; Eye Movements; Glues; Goals; Human; Image; Inferior; Knowledge; Left; Light; Link; Location; Maps; Measurement; Measures; Methods; Mind; Modeling; Neurologic; Pattern; Perception; Physiological; Physiology; Play; Prefrontal Cortex; Process; Property; Psychophysics; Reaction Time; Resolution; Role; Sensory; Series; Short-Term Memory; Signal Transduction; Source; Stress Tests; Technology; Telephone; Temporal Lobe; Testing; Time; Training; Update; Variant; Vision; Visual; Visual Cortex; Visual Psychophysics; Visual impairment; Visual system structure; Work; attentional modulation; base; behavior measurement; cognitive function; convolutional neural network; experience; experimental study; luminance; neural circuit; neuromechanism; neurophysiology; novel; object recognition; predicting response; relating to nervous system; response; sample fixation; sensory input; sensory stimulus; spatiotemporal; theories; visual information; visual search

Project Summary Our ability to visually interpret the world around us depends on rapid 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, invasive electrical stimulation, and computational models. We focus on the ubiquitous challenge of visual search, exemplified by searching for your phone using exclusively visual cues. The behavioral data will provide critical constraints about human integrative abilities, particularly through eye movements and the dynamics of recognition and object location. The invasive neurophysiological data will provide high spatiotemporal resolution of neural activity along the inferior temporal cortex and the interactions with the pre- frontal cortex, which are hypothesized to be critical for conveying the type of top-down signals required for recognition and attention modulation during visual search. Ultimately, a central goal of our proposal is to formalize our understanding of these integrative processes 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 progress towards elucidating the mechanisms underlying pattern completion used by the visual system to infer the identity of objects from partial information, the effects of contextual information during object recognition, and computational models of visual search. 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, the proposed work aims to establish a strong computational, behavioral and physiological framework that merges bottom-up and top-down processing. Furthermore, we will move beyond correlative measures by using electrical stimulation to stress test the models and establish causal links between key nodes in the circuitry and visual search behavior. 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快速的自下而上的计算， 从感官输入中提取相关信息，但这也取决于我们积累的核心知识 关于世界提供基于先前经验的自上而下的信号。本提案的目的是研究 视觉信息在空间上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、侵入性电刺激和计算模型。我们专注于无处不在的挑战， 视觉搜索，例如搜索您的手机使用专门的视觉线索。行为数据将 提供了关于人类综合能力的关键限制，特别是通过眼球运动和 识别和物体定位的动力学。侵入性神经生理学数据将提供高 时空分辨率的神经活动沿着下颞叶皮层和相互作用的前 额叶皮层，这是假设是至关重要的传递类型的自上而下的信号所需的 视觉搜索过程中的识别和注意力调节。最终，我们提案的一个中心目标是 通过定量计算模型，使我们对这些综合过程的理解正规化。这 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。在本奖项期间，我们在阐明潜在机制方面取得了进展 视觉系统用来从部分信息推断物体身份的模式完成， 在物体识别过程中的上下文信息，以及视觉搜索的计算模型。我们有强大 初步证据表明，最先进的纯自下而上的承认理论， 深度卷积网络无法解释人类的行为和生理。因此，拟议的工作 旨在建立一个强大的计算，行为和生理框架，融合自下而上， 自上而下的处理。此外，我们将通过使用电刺激来超越相关措施， 对模型进行压力测试，并在电路和视觉搜索的关键节点之间建立因果联系 行为理解核心知识被纳入感官的神经机制 处理可以说是认知科学中最大的挑战之一， 对许多神经和精神疾病的影响，这些疾病的特征是自上而下的功能失调， 信号和仍然知之甚少。