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动物早期视觉皮层的神经生理学研究相结合，以研究所观察到的神经表示和计算架构的功能，编码和计算优势。 神经模型，与经常性的连接和建议的双代码策略，将开发解决多个视觉问题，同时适应神经生理数据。 表示将从编码的角度和计算的角度进行研究，根据场景统计和解决视觉问题的相关性。该研究计划将通过国际合作促进，并与神经计算的本科和研究生教育紧密结合。 拟议的项目范围为生物视觉系统的计算和功能提供了新的见解，以及开发机器学习系统的新思路和灵感，这些系统可以从有限的数据中学习，并在新的复杂情况下稳健灵活地发挥作用，目前的深度学习神经网络利用数十或数百层来学习特定计算机视觉的解决方案。问题哺乳动物的视觉系统层次要少得多，但却能在各种新奇复杂的情况下解决许多任务。神经系统可能通过具有环路和循环连接的神经元回路以及每一层中数量级更多的神经元来实现这一壮举。“最近的神经生理学研究结果表明，灵长类动物初级视觉皮层（V1）中的神经元并不是教科书中描述的简单定向边缘和条形探测器，而是对高度特定的复杂局部模式有强烈的反应，尽管它们也对许多其他模式有较弱的反应。 PI提出，单个神经元不是无定形的实体，在大群体中无脸地发挥作用，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。他们参与人口编码的信息与强稀疏代码或弱分布式代码分别取决于他们service.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

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.