RI: Small: Computational and Physiological Studies of Complex Neural Codes in the Early Visual Cortex

在这个跨学科的项目中，机器学习方法与灵长类早期视觉皮质的神经生理学研究相结合，以调查观察到的神经表示和计算结构的功能、编码和计算优势。带有循环连接和提出的双码策略的神经模型将被开发出来，以同时解决多个视觉问题，并符合神经生理学数据。基于场景统计和它们与解决视觉问题的相关性，将从编码和计算的角度来研究表示法。该研究计划将由国际合作促进，并与神经计算方面的本科生和研究生教育紧密结合。该项目为生物视觉系统的计算和功能提供了新的见解，也为开发能够从有限的数据中学习并在新颖的复杂情况下稳健而灵活地运行的机器学习系统提供了新的思路和灵感，具有潜在的广泛的社会和技术影响。目前的深度学习神经网络利用数十或数百个层次来学习特定计算机视觉问题的解决方案。哺乳动物的视觉系统层次要少得多，但仍能在各种新颖和复杂的情况下解决许多任务。神经系统可能会通过神经回路和循环连接来实现这一壮举，每一层中都有更多的神经元。最近的神经生理学发现表明，灵长类动物初级视觉皮质(V1)中的神经元并不像教科书中描述的那样简单地定向边缘和条形探测器，而是对高度特定的复杂局部模式做出强烈的反应，尽管它们也对许多其他模式做出反应，但反应要弱得多。PI提出，单个神经元不是无定形的实体，在一个大的群体中毫无表情地发挥作用，而是独特而独特的个体，在某些特定任务中充当专家，在其他任务中充当通才。根据他们所服务的职能角色，他们分别以强稀疏码或弱分布式码参与信息的总体编码。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In this interdisciplinary project, machine learning approaches are coupled with neurophysiological studies of primate early visual cortex to investigate the functional, coding and computational benefits of the observed neural representation and computing architecture. Neural models, with recurrent connections and the proposed dual-code strategy, will be developed to solve multiple vision problems simultaneously and to fit neurophysiological data. The representations will be studied from both coding perspectives and computational perspectives, based on scene statistics and their relevance for solving vision problems. The research program will be facilitated by international collaboration and tightly integrated with undergraduate and graduate education in neural computation. The proposed project wide provide new insights to the computations and functions of the biological visual system, as well as new ideas and inspirations for developing machine learning systems that can learn from limited data and function robustly and flexibly in novel complex situations, potentially with broad societal and technological impact.Current deep learning neural networks utilize tens or hundreds of layers to learn solutions for specific computer vision problems. The mammalian visual system has much fewer layers, and yet can solve many tasks in a variety of novel and complex situations. The nervous system might achieve this feat by having neuronal circuits with loops and recurrent connections, and with order of magnitude more neurons in each "layer." Recent neurophysiological findings suggest that neurons in the primary visual cortex (V1) of primates are not simply oriented edge and bar detectors as described in textbooks, but respond strongly to highly specific complex local patterns, although they also respond to many other patterns with much weaker responses. The PI proposed that the individual neurons are not amorphous entities, functioning facelessly in a large population, but are distinct and unique individuals that serve as specialists for some specific tasks and as generalists in other tasks. They participate in population encoding of information with strong sparse codes or weak distributed codes respectively, depending on the functional roles they serve.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.