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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.

项目总结/摘要我们的视觉系统是如何理解视网膜中接收到的复杂的光线模式的？机器视觉最近已经成功地解决了自然图像上的复杂任务，例如物体分类.因此，我们可以使用这些模型来推断转换光强度模式的计算由自然图像驱动，一直到行为输出，例如图像中的对象类别。然而，机器视觉提供的视觉模型并不能提供对细胞和分子的洞察力。视觉系统中的机制。另一方面，视觉系统的经典模型提供了生物学上忠实的解释了视觉系统在发育过程中所经历的多个阶段，从睁开眼睛到成年然而，它们的范围通常是非常有限的，并且它们不能为蜂窝网络提供功能帐户。和他们所模拟的分子机制。在这个提议中，我的目标是将物体识别的大规模模型置于生物学约束下，来理解这些约束的计算和功能作用。为此，我将重点放在理论上- 自然视觉和机器视觉都面临的优化问题的一个易于处理的方面：正则化。正则化是指优化目标的一部分，它使输入和输出之间的映射服从一些通常与资源有关的约束，生物系统的能量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视觉中具有基本的计算作用。视觉皮层，而不仅仅是维护和稳定的作用，他们往往被归因于。为了验证这一假设，我的目标是进行一系列的计算，理论和最终的实验步骤（在与实验室合作）。在目标1中，我建立了视觉皮层的大规模模型，在不同的正则化项下，特别是模仿神经元自适应的细胞级正则化约束，监管程序。然后，我根据一系列功能和大脑相似性测量来评估这些模型。在目标2，我建立了一个理论框架，以获得对这些调节机制的基本理解相互关联。最后，在Aim 3中，我开发了实验协议来验证Aim的预测 1、目标2根据我的初步结果，我假设视网膜自发活动可以发挥作用，重要的调节作用与自然视觉的功能意义，我设想视网膜的新用途假体装置作为研究视觉发育的有价值的实验工具。