Learning and updating internal visual models

 描述(由申请人提供)：根据NEI的战略计划，该项目致力于填补我们对视觉感知神经机制的深刻理解空白。具体地说，我们的目标是了解视觉皮层回路的适应如何有助于感知。适应是一个无处不在的过程，神经加工和知觉受到最近的视觉输入的极大影响。然而，人们对适应的功能目的知之甚少。基于初步数据，该项目测试了这样一种假设，即视觉适应实例化了一种形式的预测编码，用于使意外事件变得突出。我们假设，大脑皮层回路学习VisuA输入的统计结构的方式超出了以前基于疲劳的适应效应描述。这种学习被用来打折预期的特征和表示新的特征。我们的项目将通过三名具有以下专业知识的研究人员的合作努力来验证这一假设 人类脑电、动物神经生理学和计算建模。目标1将评估皮质回路适应输入的时间序列和发出偏离预期序列的信号的能力。目标2将评估刺激不确定性对适应和对新事件的反应的影响。目标3将确定刺激统计的时间恒定性如何影响适应动态和对新刺激的反应。这些目标中的每一个都涉及到一种实验操作，这种操作产生了与基于疲劳和预测性编码机制不同的行为。因此，我们的目标将共同为我们的核心假设提供一个稳健的测试，并提供对大脑皮层回路自适应特性的更丰富的理解。我们项目的结果将有助于回答视觉研究中持续存在的难题之一，即理解视觉感知中适应机制的功能目的。

 DESCRIPTION (provided by applicant): In line with the strategic plan of the NEI, this project is focused on filling a profound gap in our understanding of neural mechanisms of visual perception. Specifically, we aim to understand how the adaptation of visual cortical circuits contributes to perception. Adaptation is a ubiquitous process by which neural processing and perception are dramatically influenced by recent visual inputs. However, the functional purpose of adaptation is poorly understood. Based on preliminary data, this project tests the hypothesis that visual adaptation instantiates a form of predictive coding, which is used to make unexpected events salient. We posit that cortical circuits learn the statistical structure of visua input in a manner that extends beyond previous fatigue- based descriptions of adaptation effects. This learning is used to discount expected features and signal novel ones. Our project will test this hypothesis through the collaborative effort of three investigators with expertise in human EEG, animal neurophysiology, and computational modeling. Aim 1 will assess the ability of cortical circuits to adapt to temporal sequences of input and to signal deviations from expected sequences. Aim 2 will evaluate the effect of stimulus uncertainty on adaptation and responses to novel events. Aim 3 will determine how adaptation dynamics and responses to novel stimuli are influenced by the temporal constancy of stimulus statistics. Each of these aims involves an experimental manipulation that yields distinct behavior from fatigue- based and predictive coding mechanisms. Thus, together our aims will provide a robust test of our core hypothesis, and provide a much richer understanding of the adaptive properties of cortical circuits. Results from our project will contribute to answering one of the continuing puzzles in visual research, which is to understand the functional purpose of adaptive mechanisms in visual perception.