Is anyone interested in collaborating on a Common Lisp implementation of HTM? (using SBCL ( http://www.sbcl.org/ ), cl-graph to model the structure ( https://github.com/gwkkwg/cl-graph ) and rtg-math for the math library ( https://github.com/cbaggers/rtg-math ) with lparallel ( https://lparallel.org/ ) for parallel processing as well as the best gpu library (there are many) to enable running on gpus as well). The advantages of Common Lisp: a high level language (I would argue even better in that respect than python) with the fully compiled speed of C+ - so it’s a likely candidate for creating the fastest implementation.
I’d be willing to help on a clojure one…
The existing clojure impl has not been updated in 3 years
This is interesting. Can a newbie in SBCL and NUPIC help?
I am implementing the Spatial Pooler algorithm in SBCL just to learn.
I would love to learn more about your project.
If we get another couple people, we’ll set it up on github.
Just found this on github after talking to Marek Kochanowicz on #lisp IRC: https://github.com/sirherrbatka/cl-htm
Hi Gibson, if you need still more people I can try to contribute. I mostly use SBCL and have used lparallel lib for parallel processing. Jeosol
Another piece to work with, an algebraic topology library in lisp: http://www-fourier.ujf-grenoble.fr/~sergerar/Kenzo/ (My interest in pursuing this inspired by the Blue Brain Project video The Hidden Structure of the Neocortical Column by Kathryn Hess, EPFL
I’d be willing to help out. The idea is fascinating.
I think step one is setting up a systems model for various neuron types, the cortical column and wider systems. I’ve set up a thread to discuss this here: Systems Diagram and understanding of the Neo Cortex Mark Browne offered some great links to work done on systems understanding there starting with this one: http://www.mrc.uidaho.edu/~rwells/techdocs/Cortical%20Neurons%20and%20Circuits.pdf. The goal is to have a fully integrated systems approach to organize the effort.
Interesting work in the role of dendrites in parallel processing: https://actu.epfl.ch/news/the-way-a-single-neuron-processes-information-is-2/