fMRI and Behavioral Studies of Unsupervised Learning in High Level Visual Cortex

描述(由申请人提供)：经验被认为在塑造支持物体识别的大脑皮层表征方面发挥了关键作用，通过创建神经反应对某些方面的变化具有选择性，对另一些方面不变。尽管之前的许多研究都考察了有监督的训练对大脑对象选择区域的影响，但对于视网膜输入的统计规则在多大程度上直接塑造对象识别所涉及的神经基质，人们知之甚少。无监督学习很重要，因为它允许大脑使用简单的自组织机制，将连续的视觉输入流量转化为我们经验的稳定对象。虽然行为和计算研究强烈表明，无监督学习在物体识别中起着关键作用，但大多数相关的神经科学研究都集中在输入统计的作用上，主要集中在早期视觉区域的影响上。在这里，我们提出了结合功能磁共振成像、心理物理学和计算建模的尖端技术的实验，以检验关于对象识别中的无监督学习的两个假设。首先，我们提出，神经反应可能会被调整，以匹配在无监督训练期间所经历的形状和对象样本的范围和频率。也就是说，神经反应将增加，对于在无监督训练期间更频繁出现的项目，相对于不经常看到或未培训的项目，神经反应将变得更有选择性。这可以提供一种改进对最频繁出现的刺激的辨别性能的机制。其次，行为和计算证据表明了一个耐人寻味的假设，即大脑使用时空相关性作为一种手段，将不同的图像绑定为属于同一对象，从而允许在戏剧性的变化中识别同一对象，例如旋转导致其外观的变化。我们将确定在无监督训练期间视觉输入中的时空相关性是否增加了大脑反应和知觉表现相对于以不相关方式训练的类似项目和训练前反应(和表现)的不变性。第三，我们将研究非监督学习机制是否推广到监督学习。在我们的所有实验中，我们将检查无监督训练前后的神经反应和表现，并使用计算建模将fMRI数据与可能的潜在神经机制联系起来，如神经调谐的锐化和发射率的增加。这项拟议的工作将通过提供第一个关于神经机制的描述来填补知识的重要空白，这些神经机制从视觉经验的统计数据中产生有效的对象识别表征。

Description (provided by applicant): Experience is thought to play a critical role in shaping the cortical representations that support object recognition by creating neural responses are selective for some dimensions of change and invariant to others. Although many previous studies have examined the effects of supervised training on object selective regions of the brain, much less is known about the degree to which statistical regularities in the retinal input can directly shape the neural substrates involved in object recognition. Unsupervised learning is important because it allows the brain to employ simple self organizing mechanisms that turn the continuous flux of visual input into the stable objects of our experience. While behavioral and computational work strongly suggests that unsupervised learning plays a key role in object recognition, most related neuroscience work examining the role of input statistics has focused on its effects in early visual areas. Here we propose experiments that combine cutting edge techniques in fMRI, psychophysics, and computational modeling to examine two hypotheses concerning unsupervised learning in object recognition. First, we propose that neural responses may become tuned to match the range and frequency of shape and object exemplars experienced during unsupervised training. That is, neural responses will increase and become more selective for items seen more frequently during unsupervised training relative to infrequently seen or untrained items. This may provide a mechanism which improves discrimination performance for stimuli seen most frequently. Second, behavioral and computational evidence suggests the intriguing hypothesis that the brain uses spatio-temporal correlations as a means for binding different images as belonging to the same object, allowing for recognition of the same object across dramatic transformations, such as changes in its appearance due to rotation. We will determine if spatio- temporal correlations in the visual input during unsupervised training increases the invariance of both brain responses and perceptual performance relative to similar items trained in an uncorrelated manner and pre- training responses (and performance). Third, we will examine if mechanisms of unsupervised learning generalize to supervised learning. In all of our experiments we will examine neural responses and performance both before and after unsupervised training, and use computational modeling to link fMRI data to the possible underlying neural mechanisms such as sharpening of neural tuning and increased firing rates. The proposed work will fill important gaps in knowledge by providing the first account of the neural mechanisms that generate effective representations for object recognition from the statistics of visual experience.