CRCNS US-Japan Research Proposal: Modeling the Dynamic Topological Representation of the Primate Visual System

CRCNS 美日研究提案：灵长类视觉系统的动态拓扑表示建模

• 批准号: 2208362
• 负责人: Garrison Cottrell
• 金额: $ 68万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208362&HistoricalAwards=false
• 关键词: CRCNS; US; Japan; Research; Proposal

The goal of this project is to understand how we see by building computer models that "see the way we do." It is obvious that we learn to talk; it is less obvious that we learn to see. Babies have roughly 20/400 vision, which means they are legally blind, and the world initially looks very blurry to them. They must learn to distinguish people (especially their mother and family) as well as toys, food, and other objects over months and years of development. How is it that we come to be able to see so well that we can play ball, read a book, and thread a needle? One way to understand how this happens is to build computational models that mimic the way the brain works. Artificial Intelligence has blossomed in recent years with the advent of deep neural networks, which are a very simplified model of the brain. They are capable of recognizing faces and objects, and are enabling the creation of self-driving cars. However, there are fundamental differences between these computer vision models and our own visual system that make them less robust. This project will add more features of the human visual system to these models. For example, we have a foveated retina, which enables high fidelity vision only within a small spot of the visual field, about the size of your thumbnail at arm's length. As a result, we move our eyes about 3 times a second in order to bring the world into focus. This project will build a computational model that has a foveated retina, "moves its eyes," and takes data from brain recordings into account.Recent models of the visual system have been benchmarked against cortical recordings (CORnet, BrainScore), but appear to be reaching a plateau. To move beyond this, the next generation of models will have to come closer to the brain in both anatomy and physiology. This project will incorporate radical changes to convolutional networks as well as novel data from the primate visual system. Missing from most models of the visual system are: 1) biologically realistic lateral and feedback connections, including distinct pools of excitatory (E) and inhibitory (I) neurons with the full set of lateral interactions (E-E, E-I, I-E, I-I), and purely excitatory feedback connections; 2) the log-polar mapping from retina to V1, separating central from peripheral representations and adding rotation and scale invariance; and 3) saccades, adding dynamics to the representations. Missing from most neurophysiological recordings are 1) recordings from IT during free viewing of objects (saccading); 2) pharmacological suppression of central and peripheral V1 while recording from IT in order to measure their contributions to representations; and 3) simultaneous recording from multiple areas of IT providing crucial data on their interactions. This project will incorporate all of these advances in order to build biologically realistic vision systems.A companion project is being funded by the National Institute of Information and Communications Technology, Japan (NICT).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.

该项目的目标是通过构建“看到我们的方式”的计算机模型来了解我们如何看待。很明显，我们学会说话。我们学会看到的不太明显。婴儿的视力大约为20/400，这意味着他们在法律上是盲目的，世界最初对他们看起来很模糊。他们必须学会在数月和几年的发展中区分人（尤其是他们的母亲和家庭）以及玩具，食物和其他物品。我们如何才能看到如此出色的能力，以至于我们可以打球，读书和螺纹针头？理解这种情况的一种方法是构建模仿大脑工作方式的计算模型。近年来，随着深度神经网络的出现，人工智能已经蓬勃发展，这是一个非常简化的大脑模型。他们能够识别面孔和物体，并能够创建自动驾驶汽车。但是，这些计算机视觉模型与我们自己的视觉系统之间存在根本差异，这使它们变得不那么健壮。该项目将在这些模型中添加更多人类视觉系统的功能。例如，我们有一个散发性的视网膜，它只能在视野的一小部分内才能使您的缩略图的大小在手臂的长度上。结果，我们将目光移动到每秒3次，以使世界成为焦点。该项目将建立一个具有散发性的视网膜“移动眼睛”的计算模型，并考虑了大脑记录的数据。视觉系统的续签模型已针对皮质记录（Cornet，Brainscore）进行了基准测试，但似乎达到了平稳。为了超越这一点，下一代模型必须在解剖学和生理学中更接近大脑。该项目将结合卷积网络的根本变化以及灵长类动物视觉系统的新数据。视觉系统的大多数模型缺少：1）具有完整的横向相互作用集（E-E，E-E，I-E，I-E，I-I）的兴奋性（E）和抑制性（i）神经元的不同兴奋性（E）和抑制性（I）神经元的不同池以及纯粹的兴奋性反馈连接； 2）从视网膜到V1的对数极性映射，将中心与外围表示分开，并增加旋转和尺度不变性； 3）扫视，为表示形式增加动力。大多数神经生理记录中缺少1）在自由观看物体（扫视）期间的记录； 2）从中记录中央和周围V1的药理抑制，以衡量其对代表的贡献； 3）从其多个领域同时记录其相互作用的关键数据。该项目将纳入所有这些进步，以建立生物学现实的愿景系统。一个伴侣项目由日本国家信息与通信技术研究所（NICT）资助。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查审查标准来通过评估来通过评估来提供支持的。