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

这个项目研究了将生物视觉皮层回路的几个迄今未结合的特征结合到用于视觉处理的深层神经网络中的效果。深层神经网络是一种人工电路，松散地受到大脑皮质的启发。近年来，它们解决复杂问题的能力，比如识别视觉场景中的对象，给人工智能和机器学习带来了革命性的变化。为视觉对象识别而训练的深层网络中的层的层次结构还提供了与对象识别相关的视觉皮质区域的层次结构的最佳现有模型(“腹侧流”)。这个项目试图了解加入大脑回路的额外功能是否以及如何可以(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)产生改进的视觉皮质模型。改善深度网络的性能将在受到人工智能进步影响的广泛社会和行业范围内产生巨大好处。改进的视皮层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知觉及其潜在的增强，以及精神疾病和知觉和认知缺陷带来重大的进一步好处。深度网络目前使用几乎纯粹的前馈处理实现了它们的成功。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较高级别的区域的“绕过”连接。深层网络还使用可以兴奋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或抑制的。这个项目将把反馈和旁路连接整合到深层网络中，以及由不同的兴奋性和抑制性神经元组成的网络中的局部递归处理。研究人员最近的工作表明，局部递归加工如何解释一些经常被概括为“正常化”的非线性视觉皮质操作。目前在深层网络中使用的简单形式的归一化将活动维持在适当的动态范围内，但生物学形式的归一化涉及在确定神经响应时不同刺激特征和位置之间的相互作用，这可能具有重要的计算角色，例如在分析视觉场景时。结合这些特征的深度网络的性能将在各种视觉任务上以及作为腹侧流神经数据和人类心理物理数据的模型进行分析，并与现有的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.