Department of Psychology, Stanford University, Stanford, California, USA.
PLoS Comput Biol. 2013;9(5):e1003079. doi: 10.1371/journal.pcbi.1003079. Epub 2013 May 30.
Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli. Typically, different properties are studied and modeled separately. To integrate our knowledge, it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli. In this study, we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent (BOLD) responses in early visual cortex as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. The first stage involves well-established computations-local oriented filters and divisive normalization-whereas the second stage involves novel computations-compressive spatial summation (a form of normalization) and a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model, which are estimated from BOLD data, vary systematically across visual field maps: compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast. Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing.
视觉神经科学家通过对受控刺激反应的仔细分析，发现了神经表示的基本性质。通常，不同的性质分别进行研究和建模。为了整合我们的知识，有必要建立从输入图像开始并预测对广泛刺激的反应的通用模型。在这项研究中，我们开发了一种模型，该模型可以接受任意带通灰度图像作为输入，并预测早期视觉皮层中的血氧水平依赖 (BOLD) 反应作为输出。该模型具有级联架构，由两个线性和非线性操作阶段组成。第一阶段涉及经过充分验证的计算——局部定向滤波器和除法归一化——而第二阶段涉及新颖的计算——压缩空间求和（归一化的一种形式）和类似于方差的非线性，可产生对二阶对比度的选择性。该模型的参数是从 BOLD 数据中估计的，在视场图中系统地变化：与初级视觉皮层相比，皮层外地图通常具有更大的感受野大小、更强的归一化水平和对二阶对比度的选择性增加。我们的研究结果提供了对刺激在视觉处理的连续阶段中如何编码和转换的深入了解。
