Here are my slides for the talk I gave today at The 4th workshop on Advanced Methods in Theoretical Neuroscience, Structure and disorder: From random connections to functional circuits, July 10-12 2019, Göttingen, Germany.
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Scientific Clearing House 
Since i’m an interdisciplnary research scientist (with PhDs in Dilletantism, Procrastination and Discipl(ine)ship ) interested in education, I could relate to the discussion on ‘when learning fails’.
Seems to boil down to 3 very short equations.
The other equations in the paper look similar to ones I’ve seen before (in old papers by Cowan, Grossberg, Kuramoto , and statistical physics).
I see WAT (Stone-Weirstrauss Approximation Theorem) makes a cameo appearence. One of my favorite theorems i don’t understand (though i call it piecewise linear approximation, or you can generalize to splines, wavelets, etc. There are maybe 10-20 theorems that would cover most of the phase space) .
The ACM Turing award talk online was yesterday on ‘deep learning’ and was given by Yann Lecun (someone i had never heard of ) and likely may be related to this. (Some trace ‘deep learning’ back to to the wilson-cowan model; i trace it back to a popular article in Sci Am by John Hopfield on spin glasses and neural nets. ).
(I was told about this by someone trained at U Toronto who actually took a course by LEH Trainor. retired from industry and has a recent paper
https://arxiv.org/abs/1906.11131 I think that could be applied to learning and education if you change the word light to society. they say traffic flows follow a poisson distribution, which is a variant of gaussian.
He also had seen the ACM Turing award talk by Judea Pearl on his own ‘causal reasoning’ approach— the differences that do/n’t make a difference. Pearl says deep learning is ‘just curve fitting’.
Geoff Hinton previously of CMU shared this prize–he’s sort of connected to the ‘connectionist’ camp as opposed to innatist/chomskyian ‘ camp.
I wonder if David Hilbert attended the Gottingen lecture–eg make a cameo appearance. .
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