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An Information-Maximization Approach to Blind Separation and Blind Deconvolution
TLDR
It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived. Expand
Thalamocortical oscillations in the sleeping and aroused brain.
TLDR
Analysis of cortical and thalamic networks at many levels, from molecules to single neurons to large neuronal assemblies, with a variety of techniques, is beginning to yield insights into the mechanisms of the generation, modulation, and function of brain oscillations. Expand
Face recognition by independent component analysis
TLDR
Independent component analysis (ICA), a generalization of PCA, was used, using a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons, which was superior to representations based on PCA for recognizing faces across days and changes in expression. Expand
Analysis of fMRI data by blind separation into independent spatial components
TLDR
This work decomposed eight fMRI data sets from 4 normal subjects performing Stroop color‐naming, the Brown and Peterson word/number task, and control tasks into spatially independent components, and found the ICA algorithm was superior to principal component analysis (PCA) in determining the spatial and temporal extent of task‐related activation. Expand
A Learning Algorithm for Boltzmann Machines
TLDR
A general parallel search method is described, based on statistical mechanics, and it is shown how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way. Expand
The “independent components” of natural scenes are edge filters
TLDR
It is shown that a new unsupervised learning algorithm based on information maximization, a nonlinear "infomax" network, when applied to an ensemble of natural scenes produces sets of visual filters that are localized and oriented. Expand
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
TLDR
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 and is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain. Expand
Removing electroencephalographic artifacts by blind source separation.
TLDR
The results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. Expand
Running enhances neurogenesis, learning, and long-term potentiation in mice.
TLDR
The results indicate that physical activity can regulate hippocampal neurogenesis, synaptic plasticity, and learning. Expand
Slow Feature Analysis: Unsupervised Learning of Invariances
TLDR
Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal that is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decor-related features, which are ordered by their degree of invariance. Expand
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