This paper basically says that: > general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but no

This paper basically says that: > general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. > Our findings provide evidence for the importance of both localized lateral PFC connectivity and global functional brain network connectivity This reminds me of the thousand brains theory by Jeff Hawkins. (https://www.numenta.com/blog/2019/01/16/the-thousand-brains-theory-of-intelligence/ https://www.numenta.com/blog/2019/01/16/the-thousand-brains-...) So his paper is maybe one evidence towards this theory? I'm actually more interested in actual theories or models of neuronal intelligence. This paper does not really propose any new theory or model but just observes, as most such papers do. Which is also important, of course, but I just find less exciting. Of course, just a theory or model in itself is also not interesting, but it becomes interesting when (not necessarily both): - You can experimentally show that it is powerful (ignoring whether it is really biological accurate), e.g. all the current deep learning models, GPT and co. - You have evidence that it is biologically accurate. This paper mentions Process Overlap Theory (https://www.tandfonline.com/doi/abs/10.1080/1047840X.2016.1153946?journalCode=hpli20 https://www.tandfonline.com/doi/abs/10.1080/1047840X.2016.11...) and Network Neuroscience Theory (https://www.sciencedirect.com/science/article/pii/S1364661317302218?pes=vor https://www.sciencedirect.com/science/article/pii/S136466131...). I don't really know these, but I guess those are an interesting read.