Computational and theoretical understanding of regulatory mechanisms shaping natural vision

对塑造自然视觉的调节机制的计算和理论理解

基本信息

批准号：
10723937
负责人：
Tahereh Toosi
金额：
$ 12.33万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10723937
关键词：
Address Adult Anatomy Animal Model Area Award Behavioral Biological Biological Process Brain Categories Characteristics Classification Code Collaborations Complex Computer Models Data Data Analyses Data Collection Data Set Development Exhibits Eye Face Goals Human Image Impaired cognition Knowledge Length Light Link Maintenance Maps Measures Methods Modeling Molecular Monkeys Mus Nervous System Neural Network Simulation Neurons Noise Ocular Dominance Output Pattern Performance Phase Physiological Play Population Process Property Prosthesis Protocols documentation Resources Retina Role Series Shapes Stimulus Synapses Systems Development Testing Training Vision Visual Visual Cortex Visual System Weight Work area V1 area striata artificial neural network biological systems career design energy efficiency experimental study image processing innovation insight laboratory experiment light intensity machine vision neural neural network novel object recognition outcome prediction receptive field response retinal prosthesis sensory input theories tool vision development visual processing

项目摘要

Project Summary/Abstract How is our visual system capable of making sense out of the complex pattern of light received in the retina? Machine vision has recently been successful at solving complex tasks on natural images such as object classification. Thus, we can use these models to infer computations which transform the pattern of light intensities driven by a natural image all the way up to the behavioral output such as the category of objects in the image. However, the models of vision offered by machine vision don’t provide insight into the cellular and molecular mechanisms in the visual system. On the other hand, classical models of visual system offer biologically faithful accounts for multiple phases the visual system goes through during development, from before eye-opening to adulthood. However, their scope is often very limited and they cannot provide functional accounts for the cellular and molecular mechanism they model. In this proposal, my aim is to put large-scale models of object recognition under biological constraints to be able to understand the computational and functional roles of those constraints. To this end, I focus on a theoretically- tractable aspect of optimization problems that both natural vision and machine vision face: regularization. Regularization refers to the parts of optimization goal that subject the mapping between input and output to some constraints usually related to resources e.g., energy efficiency in biological systems or robustness to input noise in machine vision. I hypothesize that regulatory mechanisms in natural vision have a fundamental computational role in shaping the visual cortex rather than the mere maintenance and stability roles they are often attributed to. To test this hypothesis, I aim to conduct a series of computational, theoretical and eventually experimental steps (in collaboration with experimental labs). In Aim 1, I build large-scale models of the visual cortex and I train them under different regularization terms, notably cellular-level regularization constraints mimicking neuronal self- regulatory processes. I then assess those models under a battery of functional and brain similarity measures. In Aim 2, I build a theoretical framework to gain a fundamental understanding of how these regulatory mechanisms relate to each other. Finally, in Aim 3, I develop experimental protocols to validate the predictions made by Aim 1 and Aim 2. Based on my preliminary results, I hypothesize that retinal spontaneous activity can play a significant regulatory role with functional implications for natural vision and I envision a new use for retinal prosthetic devices as valuable experimental tools to study visual development.

项目摘要/摘要我们的视觉系统如何从视网膜收到的复杂的光模式中理解？机器视觉最近成功地解决了自然图像（例如对象）的复杂任务分类。这，我们可以使用这些模型来推断计算，从而改变光强度的模式一直以自然图像一直到行为输出，例如图像中的对象类别。但是，机器视觉提供的视力模型并不能洞悉细胞和分子视觉系统中的机制。另一方面，视觉系统的古典模型提供了生物学忠实的占多个阶段的视觉系统在开发过程中通过，从大开眼界到成年。但是，它们的范围通常非常有限，并且无法为蜂窝提供功能帐户和分子机制它们建模。在此提案中，我的目的是将大规模的对象识别模型放在生物学限制下，以便能够了解这些约束的计算和功能作用。为此，我专注于理论上的自然视觉和机器视觉面部的优化问题的可处理方面：调节。正则化是指优化目标的各个部分，将输入和输出之间的映射映射到某些通常与资源相关的约束，例如生物系统的能源效率或对输入噪声的鲁棒性在机器视觉中。我假设自然视觉中的调节机制在塑造中具有基本的计算作用视觉皮层而不是通常归因于它们的维护和稳定角色。测试这个假设，我旨在进行一系列计算，理论和最终实验步骤（在与实验实验室的合作）。在AIM 1中，我构建了视觉皮层的大型模型，然后训练它们在不同的调节项下，特别是模仿神经元自我的细胞级调节约束监管过程。然后，我在一系列功能和大脑相似性测量中评估了这些模型。在 AIM 2，我建立了一个理论框架，以获得对这些监管机制的基本了解彼此相关。最后，在AIM 3中，我开发了实验协议来验证AIM的预测 1和目标2。根据我的初步结果，我假设视网膜赞助活动可以发挥作用重要的监管作用具有对自然视觉的功能影响，我设想了一种新的用途假体设备是研究视觉发展的宝贵实验工具。