It's certainly true that there are strong biological analogies. The analogy between first layer conv features and neuroscience is pretty widely accepted -- a lot of theoretical neuroscience models produce the same features.(It's less clear for later layers whether they're biologically analogous. Several papers have found that the aggregate of neurons in those layers are able to predict biological neurons quite well, but I don't think we have the detailed and agreed upon a characterization of the features that exist on the biological side to make a strong feature-level case.)

The color vs black and white split also has biological analogies.

With that said, I'd hesitate to dismiss the GP comment. Separate from the color vs grayscale split, why do we observe low-frequency preferring to group with color? It seems very plausible to me that if there's a systematic artifact from how the data neural networks are trained on was compressed, that could play a role. Either way, it makes the argument that this is emerging from purely natural data and the network less clean. (One caveat is that these models are trained on very downscaled versions of larger images. Even if high-frequency data was discarded in the original, that wouldn't necessarily mean that high-frequency was discarded in the downsampled version the network sees. It would depend on details of the data processing pipeline.)

To be clear, I'm not a neuroscientist and this is all just my understanding from the ML side!