Very interesting. I was working on renormalization theory in the context of self-organized criticality - https://dobots.nl/2012/02/27/the-renormalization-group/

Very interesting. I was working on renormalization theory in the context of self-organized criticality - https://dobots.nl/2012/02/27/the-renormalization-group/ https://dobots.nl/2012/02/27/the-renormalization-group/ and swarm robotics. I didn't think of it to apply to deep networks, but it makes a lot of sense. What is not so likely in the brain is that these topographically mappings are the only ones. There are probably some mix-and-match schemes that allow integration of information on longer distances on scales different than that of the most coarse layer. Just my two cents. Oh yeah, and although physicists like fractal-like structures (the fixed point in the renormalization flow can be seen as having the same mapping between every two subsequent scales of granularity), people with real-life machine learning problems probably would like to study more temporal and dynamic aspects. When time gets introduced, things become complicated.