RI: Medium: Collaborative Research: Incorporating Biological-Motivated Circuit Motifs into Large-Scale Deep Neural Network Models of the Brain

该项目研究了将生物视觉皮层回路的几个迄今未合并的特征合并到深度神经网络中进行视觉处理的效果。深度神经网络是受大脑皮层启发的人工电路。它们解决复杂问题的能力，例如识别视觉场景中的物体，近年来已经彻底改变了人工智能和机器学习。为视觉对象识别训练的深度网络中的层次结构也提供了对象识别中涉及的视觉皮层区域层次结构（“腹侧流”）的最佳现有模型。该项目旨在了解结合大脑回路的额外功能是否以及如何（1）提高机器学习性能，特别是在比通常研究的任务更具挑战性的任务上;以及（2）产生改进的视觉皮层模型。提高深度网络的性能将为受人工智能进步影响的广泛社会和行业带来巨大利益。改进的视觉皮层模型将促进对皮层功能的理解，这可能会对理解正常的心理功能和感知及其潜在的增强，以及精神疾病和感知和认知缺陷产生显着的进一步益处。深度网络目前几乎完全使用前馈处理来取得成功。然而，帮助激发深层网络的视觉皮层腹侧流也在每个区域内使用大量的循环处理，以及从较高区域到较低区域的反馈连接，以及从较低区域到层次结构中更高的区域的“旁路”连接。深度网络还使用可以激发或抑制它们投射到的不同神经元的“神经元”，而生物神经元则完全是兴奋性或抑制性的。该项目将把反馈和旁路连接纳入深度网络，以及在单独的兴奋性和抑制性神经元网络中进行局部递归处理。研究人员最近的工作表明，局部递归处理是如何解释一些非线性视觉皮层操作的，这些操作通常被概括为“正常化”。目前在深度网络中使用的简单形式的标准化将活动保持在适当的动态范围内，但生物形式的标准化涉及不同刺激特征和位置之间的相互作用，以确定神经反应，这可能具有重要的计算作用，例如在解析视觉场景时。结合这些特征的深度网络的性能将在各种视觉任务上进行分析，并作为腹侧流神经数据和人类心理物理数据的模型，并与现有深度网络模型的性能进行比较。

This project studies the effects of incorporating, into deep neural networks for visual processing, several heretofore unincorporated features of biological visual cortical circuits. Deep neural networks are artificial circuits loosely inspired by the brain's cerebral cortex. Their abilities to solve complex problems, such as recognizing objects in visual scenes, have revolutionized artificial intelligence and machine learning in recent years. The hierarchy of layers in a deep network trained for visual object recognition also provides the best existing models of the hierarchy of areas in the visual cortex implicated in object recognition (the "ventral stream"). This project seeks to understand whether and how incorporating additional features of brain circuits may (1) improve machine learning performance, particularly on tasks that are more challenging than those typically studied; and (2) yield improved models of visual cortex. Improving the performance of deep networks would yield great benefits across wide swaths of society and industry that are impacted by advances in artificial intelligence. Improved models of visual cortex will advance understanding of cortical function, which may lead to significant further benefits for understanding normal mental functioning and perception and their potential enhancement, as well as mental illness and perceptual and cognitive deficits. Deep networks currently achieve their success using almost purely feedforward processing. Yet the visual cortical ventral stream that helped inspire deep networks also uses massive recurrent processing within each area as well as feedback connections from higher areas to lower areas and "bypass" connections from lower areas to areas multiple steps higher in the hierarchy. Deep networks also use "neurons" that can either excite or inhibit different neurons that they project to, whereas biological neurons are exclusively excitatory or inhibitory. This project will incorporate feedback and bypass connections into deep networks, as well as local recurrent processing in networks of separate excitatory and inhibitory neurons. Recent work by the investigators has shown how local recurrent processing explains a number of nonlinear visual cortical operations often summarized as "normalization." Simple forms of normalization currently used in deep networks maintain activities in an appropriate dynamic range, but the biological forms of normalization involve interactions between different stimulus features and locations in determining neural responses, which may have important computational roles e.g. in parsing visual scenes. The performance of deep networks incorporating these features will be assayed on a variety of visual tasks and as models of ventral stream neural data and human psychophysical data, and compared to performance of existing deep net models.