Hello Matt,
This is my first post on the mailing list and will be my first time attending office hours. I would also be interested in learning more about the new nupic.research repository and what content will go there. If possible as well, I have a series of questions related to swarming in Nupic, or more generally on utilizing adaptive/evolutionary optimization techniques (particle swarm optimization being one example of these techniques) to determine the best model parameters for a region of an HTM network. 1. What was the reasoning for choosing PSO as the technique to determine the model parameters as opposed to other adaptive/evolutionary optimization techniques such as genetic algorithms, simulated annealing, tabu search, genetic programming, etc...? 2. Just out of curiousity has anyone had the opportunity to experiment with some of these other techniques (that I have mentioned above) in determining the best model parameters for a region? 3. Are there generic model parameters (numeric values) that either through experimentation, common sense, or neuroscience knowledge that you have found that work well regardless of the raw input data, or the application domain in which HTM is being applied to? Thanks in advance for your help and I look forward to attending your office hours! -- Rian Shams
