READING THE NEURAL CODE IN THE MAMMALIAN VISUAL SYSTEM

解读哺乳动物视觉系统中的神经密码

基本信息

批准号：
6699008
负责人：
Yang DAN
金额：
$ 24万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2000
资助国家：
美国
起止时间：
2000-02-04 至 2006-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6699008
关键词：
action potentials cats computational neuroscience electrodes electrophysiology lateral geniculate body mathematical model neural information processing neuroanatomy neurons nucleic acid sequence sensory mechanism visual cortex visual pathways voltage /patch clamp

项目摘要

A major challenge in studying sensory processing is to understand the meanings of the neural messages encrypted in the spiking activity of neurons. The ability to "read the neural code" is essential for our understanding of the neural mechanisms underlying brain functions. The proposed project aims to understand the neural code in the early visual pathway, i.e., the lateral geniculate nucleus (LGN) and the primary visual cortex. We will use a combination of experimental and computational approaches to address this problem from two directions. In the forward direction, we will use a computational method to characterize more systematically features of visual inputs that are encoded in neuronal responses. In the reverse direction, we will reconstruct visual inputs from the recorded neuronal activity. In part 1, we will use decoding techniques to reconstruct spatiotemporal natural scenes from ensemble responses in the LGN. In part 1, we will use decoding techniques to reconstruct spatiotemporal natural scenes from ensemble response in the LGN. The optimal linear decoding technique, which has been used successfully in a preliminary study, will be applied to further explore the functions of precisely correlated spiking in the LGN in visual coding. We will also explore the use of a gradient descent learning algorithm to train artificial neural networks to perform optimal input reconstruction. In part 2, we will use a covariance matrix analysis to systematically characterize the features of visual inputs that are represented by the responses of primary visual cortical neurons. Since most of the cortical neurons are complex cells with highly non-linear responses, a complete description of their coding properties is difficult to obtain with conventional methods. The covariance matrix analysis may help to reveal previously unknown coding properties and may provide new insights into the nature of the neural code in the primary visual cortex. Finally, in part 3, we will apply decoding techniques to reconstruct natural scenes using properties identified by both conventional techniques and by the covariance matrix analysis. This will provide a critical test of our computational model of cortical visual coding developed in part 2. The large amount of information on the anatomy and physiology of the early visual pathway makes it an idea model for computational analysis for neural coding. The results from the proposed studies are likely to provide new insights into the general principles of sensory coding and may advance our understanding of the neuronal mechanisms of higher brain functions under normal and pathological conditions.

研究感觉处理的一个主要挑战是了解神经元尖峰活动中的神经信息的含义。 “阅读神经代码”的能力对于我们对脑功能的神经机制的理解至关重要。拟议的项目旨在了解早期视觉途径中的神经代码，即侧向基因核（LGN）和主要视觉皮层。我们将使用实验和计算方法的组合来从两个方向解决此问题。在正方向上，我们将使用一种计算方法来表征在神经元反应中编码的视觉输入的更系统的特征。在相反的方向上，我们将从记录的神经元活动中重建视觉输入。在第1部分中，我们将使用解码技术来从LGN中的集合响应中重建时空自然场景。在第1部分中，我们将使用解码技术从LGN中的集合响应中重建时空自然场景。最佳的线性解码技术已成功用于初步研究，将用于进一步探索LGN在视觉编码中精确相关的尖峰的功能。我们还将探讨使用梯度下降学习算法来训练人工神经网络以执行最佳输入重建。在第2部分中，我们将使用协方差矩阵分析来系统地表征由主要视觉皮质神经元响应表示的视觉输入的特征。由于大多数皮质神经元是具有高度非线性响应的复杂细胞，因此很难通过常规方法获得对其编码特性的完整描述。协方差矩阵分析可能有助于揭示以前未知的编码属性，并可能为主要视觉皮层中神经代码的性质提供新的见解。最后，在第3部分中，我们将使用传统技术和协方差矩阵分析鉴定的属性应用解码技术来重建自然场景。这将为我们在第2部分中开发的皮质视觉编码的计算模型提供批判性测试。关于早期视觉途径的解剖学和生理学的大量信息使其成为用于神经编码的计算分析的想法模型。拟议的研究的结果可能会提供有关感觉编码的一般原理的新见解，并可能提高我们对正常和病理条件下较高大脑功能的神经元机制的理解。