I was fascinated with this paper when it came out and reached out to the author to get access to the source. I subsequently re-implemented the python in JS for

I was fascinated with this paper when it came out and reached out to the author to get access to the source. I subsequently re-implemented the python in JS for fun and to learn how it works. This is the best post I have found that covers in detail how the algorithm works. https://medium.com/@jaiyamsharma/efficient-nearest-neighbors-inspired-by-the-fruit-fly-brain-6ef8fed416ee https://medium.com/@jaiyamsharma/efficient-nearest-neighbors... The really interesting thing from this research imho is that it is an algorithm derived from observed neural activity as opposed to most ANNs that are merely inspired by neural structure. From Saket Navlakha's page https://snl.salk.edu/~navlakha/ https://snl.salk.edu/~navlakha/ "We work at the interface of theoretical computer science, machine learning, and systems biology. We primarily study "algorithms in nature", i.e., how collections of molecules, cells, and organisms process information and solve computational problems." The whole range of neural nets feel like they came out of a process of random recipes that got thrown at the wall and we all celebrate the things that stick. It does not feel like this approach is going to lead to AGI. I am far more interested in the "algorithms in nature" kind of research where we will eventually (given the right tools) find some really interesting new advances in neural net based algorithms and potentially completely new architectures.