Independent component analysis at the neural cocktail party

'Independent component analysis' is a technique of data transformation that finds independent sources of activity in recorded mixtures of sources. It can be used to recover fluctuations of membrane potential from individual neurons in multiple-detector optical recordings. There are some ex...

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Bibliographic Details
Published in:Trends in Neurosciences 2001, Vol.24 (1), p.54-63
Main Authors: Brown, Glen D., Yamada, Satoshi, Sejnowski, Terrence J.
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Algorithms
Analysis
Animals
Biological and medical sciences
Brain - physiology
Electroencephalography - methods
Fundamental and applied biological sciences. Psychology
General aspects. Models. Methods
Humans
Linear Models
Membrane Potentials - physiology
Models, Neurological
Neural networks
Neurons - physiology
Vertebrates: nervous system and sense organs
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Summary:'Independent component analysis' is a technique of data transformation that finds independent sources of activity in recorded mixtures of sources. It can be used to recover fluctuations of membrane potential from individual neurons in multiple-detector optical recordings. There are some examples in which more than 100 neurons can be separated simultaneously. Independent component analysis automatically separates overlapping action potentials, recovers action potentials of different sizes from the same neuron, removes artifacts and finds the position of each neuron on the detector array. One limitation is that the number of sources--neurons and artifacts--must be equal to or less than the number of simultaneous recordings. Independent component analysis also has many other applications in neuroscience including, removal of artifacts from EEG data, identification of spatially independent brain regions in fMRI recordings and determination of population codes in multi-unit recordings.
ISSN:0166-2236
1878-108X
DOI:10.1016/S0166-2236(00)01683-0