Our goal was never to optimize for performance. There's a long standing hypothesis that topographic structure in the human brain leads to metabolic efficiency. Thanks to topography in ANNs, we were able to test out this hypothesis in a computational setting.
> sketchy story this is "brain like".
we reproduce the hallmarks of functional organization seen in the visual and language cortex of the brain. I encourage you to read the paper before making such comments
I did read the paper. I really hope I don't get assigned to be a reviewer for it.
You don't reproduce anything about the functional organization of the visual or language cortex. You make a pretty picture with blobs in it. And one that's trivial to get from current methods. If you think "the functional organization of the visual or language system" means random blobs of activation/connectivity, well, then it's time for a class on neuroscience. I cannot imagine what neuroscientist would let this fly reviewing the paper.
The whole "we don't optimize for performance" is nonsense. Take any modern method that prunes weights and beats your approach with ease. Then smooth its output a bit to make nice blobs. The performance loss from smoothing will still beat your method and look "brain-like" by your definition. There you go. Your experiments don't show anything at all aside from the fact that a bad method performs poorly.
You didn't think through controls or alternative hypotheses. You didn't take into account a decade of research on methods to prune networks. You don't take seriously what we know about functional organization in the brain.
All sorts of bad papers make it through reviewing these days. But.. you can definitely do better. Good luck!
> sketchy story this is "brain like".
we reproduce the hallmarks of functional organization seen in the visual and language cortex of the brain. I encourage you to read the paper before making such comments