Learning and updating internal visual models

 描述（由申请人提供）：根据NEI的战略计划，该项目的重点是填补我们对视觉感知神经机制的理解的深刻差距。具体来说，我们的目标是了解视觉皮层回路的适应如何有助于感知。适应是一个普遍存在的过程，通过这个过程，神经处理和感知会受到最近视觉输入的显著影响。然而，人们对适应的功能目的知之甚少。基于初步的数据，这个项目测试的假设，视觉适应实例化的一种形式的预测编码，这是用来使意外事件突出。我们认为，皮层回路学习视觉输入的统计结构的方式，超出了以前的疲劳为基础的适应性影响的描述。这种学习被用来对预期的特征进行折扣，并发出新的信号。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EEG、动物神经生理学和计算建模。目标1将评估皮层回路适应输入的时间序列和预期序列的信号偏差的能力。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将确定如何适应动态和反应的新刺激的影响，刺激统计的时间恒定性。这些目标中的每一个都涉及实验操作，该实验操作从基于疲劳和预测编码机制产生不同的行为。因此，我们的目标将为我们的核心假设提供一个强大的测试，并提供更丰富的理解皮层回路的自适应特性。从我们的项目的结果将有助于回答在视觉研究，这是理解视觉感知的自适应机制的功能性目的的持续困惑之一。

 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.