IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560012, India.
Center for Neuroscience, Indian Institute of Science, Bangalore 560012, India.
eNeuro. 2024 Jul 25;11(7). doi: 10.1523/ENEURO.0417-23.2024. Print 2024 Jul.
The role of gamma rhythm (30-80 Hz) in visual processing is debated; stimuli like gratings and hue patches generate strong gamma, but many natural images do not. Could image gamma responses be predicted by approximating images as gratings or hue patches? Surprisingly, this question remains unanswered, since the joint dependence of gamma on multiple features is poorly understood. We recorded local field potentials and electrocorticogram from two female monkeys while presenting natural images and parametric stimuli varying along several feature dimensions. Gamma responses to different grating/hue features were separable, allowing for a multiplicative model based on individual features. By fitting a hue patch to the image around the receptive field, this simple model could predict gamma responses to chromatic images across scales with reasonably high accuracy. Our results provide a simple "baseline" model to predict gamma from local image properties, against which more complex models of natural vision can be tested.
伽马节律（30-80Hz）在视觉处理中的作用存在争议；条纹和色调补丁等刺激会产生强烈的伽马，但许多自然图像则不会。能否通过将图像近似为条纹或色调补丁来预测图像的伽马响应？令人惊讶的是，这个问题仍然没有答案，因为伽马对多个特征的联合依赖性还没有被很好地理解。我们在两只雌性猕猴呈现自然图像和沿多个特征维度变化的参数刺激时记录了局部场电位和脑电图。对不同的条纹/色调特征的伽马响应是可分离的，这允许基于单个特征的乘法模型。通过在感受野周围拟合色调补丁，可以用这种简单的模型以相当高的精度预测跨尺度的色觉图像的伽马响应。我们的结果提供了一个简单的“基线”模型，可以根据局部图像属性预测伽马，从而可以对更复杂的自然视觉模型进行测试。
