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Hi All,
Excited to get my hands on some code to try out 
I am wondering how the following area of research relates to the Thousand Brains Theory? The structural model and free energy minimization concepts seem very related and may have some design features that are important to the AI bus?
Cortical development in the structural model and free energy minimization
Note: I work in computer science that touchs on neuroscience but it is not my field of expertise. I am picking my way through the paper myself, so I am more interested on your take on this “general” area to give me some perspective i.e. is it related? complementary? Or a different direction?
Cheers,
Paul
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Hi Paul, thanks for your question! The Free-Energy Principle and Bayesian theories of the brain are certainly interesting, and broadly compatible with the principles of the Thousand Brains Theory. In general our view however is that, while they might be useful for some neuroscience research, Bayesian approaches are often too broad and require problematic assumptions (such as modelling noise as Gaussian) for building a practical, intelligent system. While the concept of the neocortex as a system that predicts the nature of the of world is common to the Free-Energy Principle and the Thousand Brains Theory (as well as much older ideas going back to Hermann von Helmholt), we try to emphasize the key elements that really set the TBT apart, such as the use of a modular, repeating architecture that leverages structured reference frames to model entire objects.
I’m not very familiar with the Structural Model mentioned in the paper, but it might be something interesting for us to look at in terms of principles of cortical anatomy. As an aside - maybe it’s already clear, but it’s worth noting that the use of “structure” in this context is different from the concept of a reference frame.
Hope that all makes sense!
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Yes thanks 
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