Hierarchal decomposition of neural data using boosted mixtures of independently coupled hidden markov chains

摘要: In this talk, we propose a simple algorithm that takes multidimensional neural input and decomposes the joint probability into marginals using Independently Coupled Hidden Markov chains. The algorithm then uses techniques from boosting to create hierarchal dependencies between these marginal subspaces. Finally, borrowing ideas from mixture of experts, the local information is weighted and incorporated into an ensemble decision. Our results show that this algorithm is very simple to train and computationally efficient, while also providing the ability to reduce the input dimensionality for brain machine interfaces.