An image-computable model for the stimulus selectivity of gamma oscillations
An image-computable model for the stimulus selectivity of gamma oscillations
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DOI：
10.7554/elife.47035
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发表时间：
2019-11-08
期刊：
影响因子：
7.7
通讯作者：
Winawer, Jonathan
中科院分区：
文献类型：
--
作者：
Hermes, Dora;Petridou, Natalia;Winawer, Jonathan
视觉皮层中的伽马振荡被认为对于感知、认知和信息传递至关重要。然而，对视觉皮层这些振荡的观察结果差异很大。一些研究报告很少或没有刺激引起的窄带伽马振荡，其他研究报告仅某些刺激的振荡，还有一些研究报告大多数刺激的振荡较大。为了更好地理解该信号，我们开发了一个模型，可以预测任意图像的伽马响应，并根据来自人类视觉皮层的皮层电图 (ECoG) 数据验证该模型。该模型计算空间汇集方向通道输出的方差，并准确预测 86 个图像的伽玛幅度。对于一小部分刺激，伽玛响应很大，与 fMRI 和 ECoG 宽带（非振荡）响应显着不同。我们提出视觉皮层中的伽马振荡作为增益控制的生物标志物，而不是作为传达视觉信息的基本机制。
Gamma oscillations in visual cortex have been hypothesized to be critical for perception, cognition, and information transfer. However, observations of these oscillations in visual cortex vary widely; some studies report little to no stimulus-induced narrowband gamma oscillations, others report oscillations for only some stimuli, and yet others report large oscillations for most stimuli. To better understand this signal, we developed a model that predicts gamma responses for arbitrary images and validated this model on electrocorticography (ECoG) data from human visual cortex. The model computes variance across the outputs of spatially pooled orientation channels, and accurately predicts gamma amplitude across 86 images. Gamma responses were large for a small subset of stimuli, differing dramatically from fMRI and ECoG broadband (non-oscillatory) responses. We propose that gamma oscillations in visual cortex serve as a biomarker of gain control rather than being a fundamental mechanism for communicating visual information.