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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
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 during… Expand
Face recognition by independent component analysis
- M. Bartlett, J. Movellan, T. Sejnowski
- Medicine, Computer Science
- IEEE Trans. Neural Networks
- 1 November 2002
A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Expand
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 the… Expand
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
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
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 EEG… Expand
Running enhances neurogenesis, learning, and long-term potentiation in mice.
- H. van Praag, B. Christie, T. Sejnowski, F. Gage
- Medicine, Biology
- Proceedings of the National Academy of Sciences…
- 9 November 1999
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) were… Expand
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
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 human… Expand