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

项目摘要 神经科学正在进入一个新的时代，大规模的神经网络模型可以用前所未有的速度进行测试。 丰富的神经活动测量。这一建议提出了一种将理论与 在这个新时代的实验，并应用该方法论的问题，灵长类动物的人脸识别。人脸识别 问题是神经科学和人工智能交叉的一个重要问题，并提供了一个理想的领域， 解决对象识别的更普遍的问题：人脸识别的问题被限制在一个特定的 刺激类别（面部），并受到大脑中面部区域的已知网络的约束。该项目是一个合作- 在计算建模和神经生理学方面具有互补优势的两个实验室之间的比例 在fMRI识别的艾德面部区域，其共同的重点和过去的工作提供了一个坚实的基础上建立。 从理论到实验，我们将在神经网络模型中实现计算理论，并使用优化 技术来创建一组合成的面部刺激，这些刺激引起来自模型的强烈分歧的预测。我们 将这些刺激称为有争议的刺激，因为它们被优化以使模型不一致。争议 刺激提供了模型的分布外探针，并增加了我们区分替代方案的能力 计算假设我们将测试人脸识别的前馈和递归计算机制 通过在同时受到生物学（解剖学连接）约束的神经网络模型中实现它们， 和神经生理学）和认知功能（识别目标和计算约束）。目标1将 在前馈和递归神经网络模型中实现人脸识别的计算理论， 为了使理论在解释成功承认的能力和 能够解释灵长类动物面部斑块中的神经群体代码。目标2将通过记录来比较模型 由合成面部刺激引起的面部斑块中的神经反应，这些面部刺激针对模型进行了优化， 对比预测目标3是揭示剩余的限制，最好的模型，为每个人脸补丁， 记录实验，其中刺激在闭环中适应，以便最大化经验预测 模型的误差。这项工作的预期成果包括识别计算机制， 灵长类动物面部识别的基本原理，新型计算架构的发展，以及 有争议的刺激方法作为神经记录的一般实验方法， 强大的直接测试在神经网络模型中实现的计算理论。计算和 方法学的见解预计将有助于新的诊断和治疗方法的发展， ods用于脸盲（面孔失认症）和其他知觉障碍，并可能导致新的方法， 神经病学和精神病学的决策。

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.