Revealing the mechanisms of primate face recognition with synthetic stimulus sets optimized to compare computational models

通过优化比较计算模型的合成刺激集揭示灵长类动物面部识别的机制

• 批准号: 10524626
• 负责人: Winrich Freiwald
• 金额: $ 256.81万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10524626
• 关键词: Algorithms; Anatomy; Architecture; Area; Artificial Intelligence; BRAIN initiative; Biological; Biology; Brain; Cells; Characteristics; Code; Collaborations; Communities; Computer Models; Computer Vision Systems; Computing Methodologies; Data; Data Set; Decision Making; Development; Face; Face Processing; Failure; Foundations; Fragile X Syndrome; Functional Magnetic Resonance Imaging; Human; Image; Intelligence; Laboratories; Lead; Life; Link; Macaca; Measurement; Mental disorders; Methodology; Methods; Mission; Modeling; National Institute of Mental Health; Neural Network Simulation; Neurobiology; Neurodevelopmental Disorder; Neurology; Neurosciences; Outcome; Perceptual Disorders; Physiological; Population; Primates; Problem Solving; Procedures; Process; Property; Prosopagnosia; Psychiatry; Public Health; Recurrence; Research; Resources; Role; Sampling; Shapes; Signal Transduction; Social Interaction; Social Perception; Stimulus; Stream; System; Techniques; Technology; Testing; Texture; Time; Training; Uncertainty; United States National Institutes of Health; Update; Well in self; Williams Syndrome; Work; adjudicate; artificial neural network; autism spectrum disorder; cognitive function; deep neural network; developmental prosopagnosia; disability; experimental study; face perception; fitness; improved; insight; method development; neural model; neural network architecture; neuromechanism; neurophysiology; novel; novel diagnostics; novel strategies; object recognition; predictive modeling; recurrent neural network; relating to nervous system; response; social; social deficits; success; theories

Project Summary Neuroscience is entering a new era, where large-scale neural network models can be tested with unprecedent- edly rich measurements of neural activity. This proposal develops a general methodology for linking theory to experiment in this new era and applies the methodology to the problem of primate face recognition. Face recogni- tion is an important problem at the intersection of neuroscience and AI, and provides an ideal domain in which to tackle the more general problem of object recognition: the problem of face recognition is confined to a particular stimulus class (faces) and constrained by a known network of face areas in the brain. The project is a collabo- ration between two laboratories with complementary strengths in computational modeling and neurophysiology in fMRI-identified face areas, whose shared focus and past work provide a strong foundation to build on. To link theory to experiment, we will implement computational theories in neural network models and use optimization techniques to create sets of synthetic face stimuli that elicit strongly divergent predictions from the models. We refer to such stimuli as controversial stimuli since they are optimized to make models disagree. Controversial stimuli provide out-of-distribution probes of the models and increase our power to distinguish between alternative computational hypotheses. We will test feedforward and recurrent computational mechanisms of face recognition by implementing them in neural network models simultaneously constrained by biology (anatomical connectivity and neurophysiology) and cognitive function (recognition objective and computational constraints). Aim 1 will implement computational theories of face recognition in feedforward and recurrent neural network models, so as to render the theories testable in terms of both their ability to account for successful recognition and their ability to explain neural population codes in primate face patches. Aim 2 will compare the models by recording neural responses in face patches elicited by synthetic face stimuli that are optimized for the models to make contrasting predictions. Aim 3 is to reveal the remaining limitations of the best models for each face patch in recording experiments where the stimuli are adapted in a closed loop, so as to maximize the empirical prediction error of the models. The expected outcomes of this work include the identification of the computational mecha- nisms of primate face recognition, the development of novel computational architectures, and the development of the method of controversial stimuli as a general experimental methodology for neural recordings that enables powerful direct tests of computational theories implemented in neural network models. The computational and methodological insights are expected to contribute to the development of new diagnostic and treatment meth- ods for face blindness (prosopagnosia) and other perceptual disorders and could lead to new approaches for decision-making in neurology and psychiatry.

项目摘要 神经科学正在进入一个新时代，可以用前所未有的大规模神经网络模型进行测试 对神经活动的富裕度量。该建议开发了将理论与 在这个新时代的实验，并将方法应用于主要面部识别问题。面对识别 - 在神经科学和AI的交集中，这是一个重要的问题，并提供了一个理想的领域 解决对象识别的更普遍的问题：面部识别的问题已确定为特定 刺激类（面），并受到大脑面部面积的已知网络的约束。该项目是一个合作 在计算建模和神经生理学中具有互补优势的两个实验室之间的评估 在fMri识别的面部地区，其共同的重点和过去的工作为基础提供了坚实的基础。链接 实验的理论，我们将在神经网络模型中实施计算理论并使用优化 创建一组合成面部刺激的技术，从模型引起强烈不同的预测。我们 将这种刺激引用为有争议的刺激，因为它们被优化以使模型不同意。 刺激提供了模型的分布问题，并增加了我们的力量以区分替代方案 计算假设。我们将测试面部识别的进食和经常性计算机制 通过在神经网络模型中实现它们，仅受生物学的约束（解剖连接 和神经生理学）和认知功能（识别目标和计算约束）。目标1意志 进食和复发性神经网络模型中面部识别的实施计算理论，因此 至于使理论可以根据其成功认可的能力及其能力来检验 AIM 2将通过录制来比较模型 合成面部刺激引起的面部斑块中的神经反应，这些刺激被优化，以使模型制作 对比预测。 AIM 3是揭示每个面部贴片的最佳模型的剩余局限性 记录刺激在封闭环中适应的实验，以最大程度地提高经验预测 模型的错误。这项工作的预期结果包括识别计算机械的识别 主要面部识别，新计算体系结构的发展和发展 有争议的刺激方法是一种通用的实验方法，用于神经记录 在神经网络模型中实施的计算理论的强大直接测试。计算和 预计方法论上的见解将有助于开发新的诊断和治疗方法 - 面部失明（Prosopagnosia）和其他知觉障碍的ODS，可能导致新方法 神经病学和精神病学的决策。