2011
NIPS
NeurIPS 2011
On the Analysis of Multi-Channel Neural Spike Data
Abstract
Nonparametric Bayesian methods are developed for analysis of multi-channel spike-train data, with the feature learning and spike sorting performed jointly. The feature learning and sorting are performed simultaneously across all channels. Dictionary learning is implemented via the beta-Bernoulli process, with spike sorting performed via the dynamic hierarchical Dirichlet process (dHDP), with these two models coupled. The dHDP is augmented to eliminate refractoryperiod violations, it allows the “appearance” and “disappearance” of neurons over time, and it models smooth variation in the spike statistics.
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Keyword Pioneer
— beta-bernoulli process
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Interdisciplinary Bridge
— Artificial Intelligence and Healthcare & Medicine and Interdisciplinary and Machine Learning
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Hot Topic Early Bird
— dictionary learning
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Healthcare & Medicine > Research > Biosignal Processing
Machine Learning > Core Methods > Feature Learning
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Interdisciplinary > Science > Neuroscience
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian