Neural and computational mechanisms underlying robust object recognition

鲁棒物体识别背后的神经和计算机制

• 批准号: 10682285
• 负责人: FRANK TONG
• 金额: $ 41.15万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10682285
• 关键词: Animals; Architecture; Automobile Driving; Behavioral; Brain; Brain Diseases; Brain Injuries; Brain Neoplasms; Classification; Computer Vision Systems; Craniocerebral Trauma; Data; Development; Disease; Evaluation; Eye; Face; Functional Magnetic Resonance Imaging; Future; Goals; Human; Image; Inherited; Knowledge; Learning; Macaca; Mediating; Medical Imaging; Methods; Modeling; Nature; Neural Network Simulation; Neurodevelopmental Disorder; Neurons; Noise; Perceptual learning; Performance; Persons; Positioning Attribute; Protocols documentation; Psychophysics; Recurrence; Regimen; Research; Resolution; Role; Sampling; Shapes; Stimulus; Stroke; Training; Vision; Vision Disorders; Visual; Visual Cortex; Visual Pathways; Visual System; Work; base; cognitive neuroscience; computational neuroscience; deep neural network; design; experience; experimental study; flexibility; human model; image processing; improved; inferotemporal cortex; nervous system disorder; neural; neural network architecture; object recognition; predictive modeling; response; visual neuroscience; visual stimulus

Deep neural networks (DNNs) for object classification have been argued to provide the most promising state- of-the-art models of the visual system, accompanied by claims that they have attained or even surpassed human-level performance. However, mounting evidence has revealed that DNNs fail catastrophically when faced with more noisy or degraded viewing conditions. By contrast, the human visual system is far more robust. To better understand and model human vision, one must determine whether the brittle nature of DNN performance arises from flaws in their architectural design, imperfections in their learning protocols, or inadequate sampling of relevant training experiences. This project will investigate the neurocomputational bases of robust object recognition, focusing on challenge conditions of visual noise and blur, to develop new DNN models that can provide a better account of human behavioral and neural responses to object images that will vary from clear to severely degraded. Both feedforward and recurrent DNN architectures will be evaluated, and the critical sets of training experiences needed for DNNs to attain robustness will be determined. In Aim 1, we will evaluate what types of DNNs can adequately predict human behavioral and neural responses to objects embedded in noise on an image-by-image basis. Correspondences between fMRI responses at multiple levels of the human visual pathway will be compared with layer-wise DNN representations to evaluate the goodness of fit for DNN model predictions. In Aim 2, we will determine what types of DNNs can better account for human behavioral and neural responses to blurry object images. We will further explore how training with blurry images modifies the visual representations learned by DNNs, leading to greater robustness to other types of image degradation and greater sensitivity to shape information. In Aim 3, we will investigate whether perceptual training with noisy or blurry objects can allow humans to acquire even greater robustness. We will then determine whether human improvements in behavioral and neural performance can be effectively modeled by DNNs that undergo comparable regimens in visual training. As a whole, this project will lead to the development of powerful new DNN models that provide a better account of human behavioral and neural responses across a wide range of challenging viewing conditions. By developing a better neurocomputational model of the intact human visual system, we will be better positioned to eventually develop models of central visual disorders, which can arise from neurodevelopmental or neurological disorders, stroke, head injury, brain tumors or other diseases. The advancement of more robust, human-like DNNs is also highly relevant to AI applications in computer vision and medical image processing.

用于对象分类的深度神经网络(DNN)被认为提供了最有希望的状态- 最先进的视觉系统模型，并声称他们已经达到甚至超过了 人类水平的表现。然而，越来越多的证据表明，DNN在以下情况下会灾难性地失败 面临更多的噪音或降级的观看条件。相比之下，人类的视觉系统远不止 很健壮。为了更好地理解和模拟人类的视觉，人们必须确定DNN的脆性本质是否 表现源于他们架构设计中的缺陷、学习协议中的不完美，或者 对相关培训经验的抽样不足。这个项目将研究神经计算 基于稳健的目标识别，重点针对视觉噪声和模糊的挑战条件，开发新的 DNN模型，可以更好地描述人类对对象图像的行为和神经反应 这将从清晰到严重退化不等。前馈和递归DNN结构都将是 评估，以及DNN获得稳健性所需的关键培训经验集将是 下定决心。在目标1中，我们将评估哪些类型的DNN可以充分预测人类的行为和 在逐个图像的基础上，对嵌入在噪声中的对象的神经反应。功能磁共振成像之间的对应关系 在人类视觉通路的多个水平上的反应将与分层DNN进行比较 表示法来评估DNN模型预测的拟合优度。在目标2中，我们将确定 DNN的类型可以更好地解释人类对模糊对象图像的行为和神经反应。我们会 进一步探索模糊图像训练如何修改DNN学习的视觉表征，从而导致 对其他类型的图像退化的鲁棒性更强，对形状信息的敏感度更高。在《目标3》中， 我们将研究用嘈杂或模糊的物体进行感知训练是否能让人类获得甚至 更强的健壮性。然后我们将确定人类在行为和神经方面的改善 在视觉训练中接受类似训练方案的DNN可以有效地模拟性能。作为一名 总体而言，该项目将导致开发功能强大的新DNN模型，从而更好地说明 人类在各种具有挑战性的观看条件下的行为和神经反应。通过开发 一个更好的完整人类视觉系统的神经计算模型，我们最终将更好地定位于 建立中枢视觉障碍的模型，可由神经发育或神经疾病引起 精神障碍、中风、头部损伤、脑瘤或其他疾病。更健壮，更像人类的进步 DNN还与计算机视觉和医学图像处理中的人工智能应用高度相关。