Neural and computational mechanisms underlying robust object recognition

用于对象分类的深度神经网络（DNN）被认为提供了最有前途的状态- 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： 面临更嘈杂或更差的观看条件。相比之下，人类的视觉系统 健壮。为了更好地理解和模拟人类视觉，必须确定DNN的脆弱性是否 绩效源于其架构设计的缺陷、学习协议的不完善，或者 相关培训经验抽样不足。这个项目将研究神经计算 鲁棒目标识别的基础，重点是视觉噪声和模糊的挑战条件，开发新的 DNN模型可以更好地解释人类对对象图像的行为和神经反应 从透明到严重降解前馈和递归DNN架构都将是 评估，DNN获得鲁棒性所需的关键训练经验集将被 测定在目标1中，我们将评估哪些类型的DNN可以充分预测人类行为和行为。 对嵌入噪声中的物体的神经反应。功能磁共振成像与 在人类视觉通路的多个级别的响应将与逐层DNN进行比较 表示来评估DNN模型预测的拟合优度。在目标2中，我们将确定 DNN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。我们将 进一步探索模糊图像的训练如何修改DNN学习的视觉表示， 对其它类型的图像退化具有更强的鲁棒性，并且对形状信息具有更高的灵敏度。在目标3中， 我们将研究是否有嘈杂或模糊的物体知觉训练可以让人类获得甚至 更强的鲁棒性。然后我们将确定人类在行为和神经方面的改善是否 性能可以通过在视觉训练中经历可比较方案的DNN来有效地建模。作为 总的来说，这个项目将导致强大的新DNN模型的开发，这些模型提供了更好的解释， 人类的行为和神经反应在广泛的具有挑战性的观看条件。发展中 一个更好的神经计算模型的完整的人类视觉系统，我们将更好地定位，最终 开发中枢视觉障碍的模型，这可能是由神经发育或神经系统引起的。 病症、中风、头部损伤、脑肿瘤或其他疾病。更健壮、更人性化的 DNN也与计算机视觉和医学图像处理中的AI应用高度相关。

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.