Neural processing of natural scenes in the visual cortex

视觉皮层自然场景的神经处理

• 批准号: 10660753
• 负责人: STEPHEN A BACCUS
• 金额: $ 39.64万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10660753
• 关键词: Affect; Amblyopia; Bayesian Prediction; Behavior; Behavioral; Biophysics; Blindness; Brain; Cells; Code; Complex; Computer Models; Data; Detection; Disease; Dyslexia; Electronics; Electrophysiology (science); Environment; Esthesia; Ethology; Eye; Eye Movements; Foundations; Goals; Head Movements; Health; Human; Knowledge; Lead; Learning; Locomotion; Machine Learning; Measures; Memory; Modeling; Motion; Motor; Motor Activity; Movement; Mus; Neurons; Neurophysiology - biologic function; Ocular Prosthesis; Pattern; Population; Preparation; Primates; Principal Investigator; Probability; Process; Property; Prosthesis; Research; Retina; Schizophrenia; Signal Transduction; Silicon; Smell Perception; Speed; Stimulus; Strabismus; Stream; Structure; System; Testing; Uncertainty; Visual; Visual Cortex; Visual System; area striata; awake; cell type; experience; experimental study; feature detection; insight; motor behavior; neural; neural model; nonhuman primate; object motion; optic flow; programs; response; sensory cortex; sensory input; sensory system; sight restoration; statistics; theories; virtual reality; virtual reality system; visual information; visual processing; visual stimulus

Abstract The retina and visual cortex represents visual information in the form of a complex set of electrical signals to support visual behavior and memory. Although we have learned a great deal about how simple visual patterns such as striped gratings lead to neural activity in the early visual system, we know little about how natural visual scenes are represented during behavior, and how the active process of gathering visual information through body and eye movements influences this process. Visual processing becomes progressively more complex towards higher levels in the brain. Compared to primates, mice combine strong motor input with visual input at an earlier level in the visual stream, the primary visual cortex. This makes the mouse visual system an accessible to system to understand how natural scenes are represented and influenced by active sensation at a level in the visual system where computational models of the neural code for natural scenes are more tractable. This proposal has two primary goals. First to determine how the neural code changes for natural scenes from the retina to the cortex with an accurate computational model that can be analyzed to determine how specific retinal cell types contribute to cortical activity for ethological computations such as determining motion direction and speed, adaptation and object motion detection. Second, to test alternative theoretically grounded hypotheses as to how motor activity influences the representation of natural scenes, including the subtraction of expected visual stimuli to create a more efficient representation, known as predictive coding, predictive or Bayesian feature detection that adjusts the detection threshold to the prior probability that visual features are present, and simple adaptation to the strength of combined signals to avoid saturation. Using high channel count silicon probes, computational models that combine known biophysical and circuit level properties with interpretable cutting edge machine learning approaches and virtual reality systems, we will gain new insight into visual processing for natural scenes in the early visual system. These results will give a quantitative picture of how the retina and visual cortex function, which will be essential in understand how diseases that affect central visual processing such as amblyopia, strabismus and schizophrenia, and reveal general principles of cortical sensory processing. The computational models established here will also be directly applicable for use in retinal and cortical visual prosthesis systems.

摘要 视网膜和视觉皮层以一组复杂的电生理信号的形式代表视觉信息。 支持视觉行为和记忆的信号。尽管我们已经了解了很多关于 简单的视觉模式，如条纹光栅，导致早期视觉系统的神经活动，我们 我对自然视觉场景在行为过程中是如何表现的，以及活动的视觉场景是如何表现的知之甚少。 通过身体和眼睛运动收集视觉信息的过程影响这个过程。 视觉处理在大脑的更高层次上变得越来越复杂。相比 对于灵长类动物来说，小鼠在视觉流的早期水平将强烈的运动输入与视觉输入相结合， 初级视觉皮层这使得鼠标视觉系统成为一个易于理解的系统 在视觉系统的某一层次上，自然景物是如何被主动感觉所表现和影响的 自然场景的神经代码的计算模型更容易处理。这项建议 有两个主要目标首先要确定神经代码如何从自然场景中变化， 视网膜到皮层的精确计算模型，可以分析，以确定如何 特定的视网膜细胞类型有助于行为学计算的皮层活动， 运动方向和速度、自适应和对象运动检测。第二，测试替代 关于运动活动如何影响自然表征的理论基础假设 场景，包括减去预期的视觉刺激以创建更有效的表示， 被称为预测编码、预测或贝叶斯特征检测， 阈值的先验概率，视觉特征是存在的，和简单的适应强度 以避免饱和。使用高通道数硅探针，计算模型 该联合收割机将已知生物物理和电路级特性与可解释的切割边缘机相结合 学习方法和虚拟现实系统，我们将获得视觉处理的新见解， 早期视觉系统中的自然场景。这些结果将给出一个定量的图片， 和视觉皮层功能，这将是必不可少的，在了解疾病是如何影响中央视觉 处理，如弱视，斜视和精神分裂症，并揭示皮层的一般原则， 感觉处理本文所建立的计算模型也可直接应用 视网膜和皮层视觉假体系统。