Natural image processing in the visual cortex

项目摘要 来自自然环境的信号由大脑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对于理解正常的皮质功能和 它是如何在精神和神经发育障碍中受损的。在以下方面取得了实质性进展 阐明简单、参数刺激的皮质处理，以及计算技术正在改进 对自然主义刺激的神经反应的描述。然而，大脑皮层种群如何编码这个复合体， 在日常生活中接收到的自然输入知觉体验在很大程度上是未知的。该项目旨在 阐明初级视觉皮质(V1)的神经元群体如何代表自然视觉输入。 到目前为止，进展主要受到两个因素的限制。首先，在自然视觉过程中，V1神经元的输入 总是嵌入在空间和时间上下文中，但V1如何将该上下文信息集成到 人们对自然的视觉输入知之甚少。第二，以前的工作几乎完全集中在单个神经元上 刺激率，但要理解皮质表征，必须考虑人口活动的结构- 在神经元之间共享并动态演变的大量试验到试验的可变性-就像这种结构 影响人口信息和认知。这个项目的中心假设是大脑皮层 响应结构受视觉环境的调节，以接近自然视觉的最佳表示 投入。为了验证这一假设，该项目结合了机器学习来量化 自然视觉输入，以及大脑皮层群体应该如何对这些图像进行编码的理论 最佳表示，以得出V1响应结构的具体的、可证伪的预测。这些预测 将通过测量V1中观看自然图像的清醒猴子的种群活动进行测试 看电影。具体目标1将确定在静态状态下空间语境对V1反应结构的调制 图像与这些图像的最佳编码是一致的，并将比较 从建议模型到备选模型。特定目标2处理动态自然输入的V1编码，以及 将测试时间语境对V1活动的调制是否与自然语言的时间结构相适应 感觉信号，这是最佳状态所必需的。因为空间和时间都同时存在于 自然视觉，特定目标3将确定自由观看动物的视觉输入统计数据，并测试时空 由这些输入引起的V1活动的相互作用。该项目将提供统一功能的首次测试 自然视觉输入的V1编码中的语境调制理论，并阐明了 到目前为止一直被忽视的自然视觉。

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.