2007
NIPS
NeurIPS 2007
An online Hebbian learning rule that performs Independent Component Analysis
Abstract
Independent component analysis (ICA) is a powerful method to decouple signals. Most of the algorithms performing ICA do not consider the temporal correlations of the signal, but only higher moments of its amplitude distribution. Moreover, they require some preprocessing of the data (whitening) so as to remove second order correlations. In this paper, we are interested in understanding the neural mechanism responsible for solving ICA. We present an online learning rule that exploits delayed correlations in the input. This rule performs ICA by detecting joint variations in the firing rates of pre- and postsynaptic neurons, similar to a local rate-based Hebbian learning rule.
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Hot Topic Early Bird
— online learning
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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, Security & Privacy, Speech & Audio
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning
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Trend Setter
— Neural Networks
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Keyword Pioneer
— temporal correlation
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Statistical Learning
Interdisciplinary > Cognitive Science > Cognitive Modeling
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Core Methods > Neural Networks
Interdisciplinary > Science > Neuroscience