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An Information-Maximization Approach to Blind Separation and Blind Deconvolution
We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. Expand
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Thalamocortical oscillations in the sleeping and aroused brain.
Sleep is characterized by synchronized events in billions of synaptically coupled neurons in thalamocortical systems. The activation of a series of neuromodulatory transmitter systems duringExpand
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Face recognition by independent component analysis
A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Expand
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Analysis of fMRI data by blind separation into independent spatial components
Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to theExpand
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A Learning Algorithm for Boltzmann Machines
The computational power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. Expand
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Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. Expand
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Removing electroencephalographic artifacts by blind source separation.
Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEGExpand
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Running enhances neurogenesis, learning, and long-term potentiation in mice.
Running increases neurogenesis in the dentate gyrus of the hippocampus, a brain structure that is important for memory function. Consequently, spatial learning and long-term potentiation (LTP) wereExpand
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Slow Feature Analysis: Unsupervised Learning of Invariances
Slow feature analysis is a new method for learning invariant or slowly varying features from a vectorial input signal. Expand
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Dynamic Brain Sources of Visual Evoked Responses
It has been long debated whether averaged electrical responses recorded from the scalp result from stimulus-evoked brain events or stimulus-induced changes in ongoing brain dynamics. In a humanExpand
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