Natural image processing in the visual cortex

项目摘要 来自自然环境的信号由皮层中的神经元群体处理。了解 这些信号和皮质活动之间的关系是理解正常皮质功能的核心， 它是如何在精神和神经发育障碍中受损的。取得实质性进展 阐明简单、参数刺激和计算技术的皮层处理正在改善 对自然主义刺激的神经反应的描述。然而，皮质群如何编码复合物， 在每天的感知体验中接收到的自然输入在很大程度上是未知的。该项目旨在 阐明自然视觉输入是如何由初级视觉皮层（V1）的神经元群表示的。 迄今取得的进展主要受到两个因素的限制。首先，在自然视觉中，V1神经元的输入 总是嵌入在一个空间和时间的背景下，但如何V1整合这种上下文信息， 人们对自然的视觉输入知之甚少。其次，以前的工作几乎完全集中在单神经元上， 放电率，但要了解皮层代表必须考虑人口活动的结构- 神经元之间共享的大量试验与试验的可变性， 影响人口的信息和看法。这个项目的核心假设是， 响应结构由视觉上下文调制，以近似自然视觉的最佳表示 输入。为了验证这一假设，该项目结合机器学习来量化 自然的视觉输入，与理论的皮质人口应该如何编码这些图像，以实现一个 最佳表示，以达到具体的，可证伪的预测V1响应结构。的预测 将通过测量清醒猴子观看自然图像的V1中的群体活动进行测试， 电影具体目标1将确定静态环境中空间背景对V1反应结构的调制是否 图像与这些图像的最佳编码一致，并且将比较 替代模型的替代模型。具体目标2解决了动态自然输入的V1编码，以及 将测试是否调制V1活动的时间背景是调谐到时间结构的自然 感官信号，这是最优性所需的。由于空间和时间同时存在， 自然视觉，特定目标3将确定自由观察动物的视觉输入统计数据，并测试时空 这些输入诱发的V1活动的相互作用。该项目将提供一个统一的功能的第一个测试 自然视觉输入V1编码中的上下文调制理论，并阐明了 自然视觉是迄今为止被忽视的。

Project Summary Signals from the natural environment are processed by neuronal populations in the cortex. Understanding the relationship between those signals and cortical activity is central to understanding normal cortical function and how it is impaired in psychiatric and neurodevelopmental disorders. Substantial progress has been made in elucidating cortical processing of simple, parametric stimuli, and computational technology is improving descriptions of neural responses to naturalistic stimuli. However, how cortical populations encode the complex, natural inputs received during every day perceptual experience is largely unknown. This project aims to elucidate how natural visual inputs are represented by neuronal populations in primary visual cortex (V1). Progress to date has been limited primarily by two factors. First, during natural vision, the inputs to V1 neurons are always embedded in a spatial and temporal context, but how V1 integrates this contextual information in natural visual inputs is poorly understood. Second, prior work focused almost exclusively on single-neuron firing rate, but to understand cortical representations one must consider the structure of population activity— the substantial trial-to-trial variability that is shared among neurons and evolves dynamically—as this structure influences population information and perception. The central hypothesis of this project is that cortical response structure is modulated by visual context to approximate an optimal representation of natural visual inputs. To test the hypothesis, this project combines machine learning to quantify the statistical properties of natural visual inputs, with a theory of how cortical populations should encode those images to achieve an optimal representation, to arrive at concrete, falsifiable predictions for V1 response structure. The predictions will be tested with measurements of population activity in V1 of awake monkeys viewing natural images and movies. Specific Aim 1 will determine whether modulation of V1 response structure by spatial context in static images is consistent with optimal encoding of those images, and will compare the predictive power of the proposed model to alternative models. Specific Aim 2 addresses V1 encoding of dynamic natural inputs, and will test whether modulation of V1 activity by temporal context is tuned to the temporal structure of natural sensory signals, as required for optimality. As both spatial and temporal are present simultaneously during natural vision, Specific Aim 3 will determine visual input statistics in free-viewing animals, and test space-time interactions in V1 activity evoked by those inputs. This project will provide the first test of a unified functional theory of contextual modulation in V1 encoding of natural visual inputs, and shed light on key aspects of natural vision that have been neglected to date.