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

本项目研究了将生物视觉皮质回路的几个迄今未被纳入视觉处理的特征纳入深度神经网络的效果。深度神经网络是受大脑皮层启发的人工电路。近年来，它们解决复杂问题的能力，比如识别视觉场景中的物体，已经彻底改变了人工智能和机器学习。训练用于视觉对象识别的深度网络中的层次结构也提供了与对象识别相关的视觉皮层区域层次结构（“腹侧流”）的最佳现有模型。该项目旨在了解是否以及如何结合大脑回路的额外特征可以(1)提高机器学习的性能，特别是在比通常研究的任务更具挑战性的任务上；(2)得到改进的视觉皮层模型。提高深度网络的性能将为受到人工智能进步影响的社会和行业的广泛领域带来巨大利益。改进的视觉皮质模型将促进对皮质功能的理解，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重大的进一步好处。深度网络目前的成功几乎完全依靠前馈处理。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，以及从高级区域到低级区域的反馈连接，并“绕过”从低级区域到等级更高的多个步骤的区域的连接。深度网络还使用“神经元”，这些神经元可以激发或抑制它们投射到的不同神经元，而生物神经元只具有兴奋性或抑制性。该项目将把反馈和旁路连接整合到深度网络中，以及单独的兴奋性和抑制性神经元网络中的局部循环处理。研究人员最近的工作表明，局部循环处理如何解释一些通常被概括为“归一化”的非线性视觉皮层操作。目前在深度网络中使用的简单归一化形式将活动维持在适当的动态范围内，但生物形式的归一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中。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.