Encyclopedia of Computational Neuroscience

2015 Edition
| Editors: Dieter Jaeger, Ranu Jung

Independent Component Analysis of Images

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6675-8_708



Independent component analysis (ICA) is a general machine learning method for analyzing the statistical structure of data or signals. It is based on a linear generative model with statistically independent and non-Gaussian latent variables. In computational neuroscience, it can be applied on small patches (windows) of ordinary photographic images to model their structure. The result is that the independent components are similar to the outputs of simple cells in the primary visual cortex (V1). Thus, ICA of natural images provides an interesting theory to explain why the response properties (receptive fields) of simple cells in V1 are as they are: they are adapted to the statistical structure of natural images. This supports the more general hypothesis that the visual cortex constructs an internal probabilistic model of the world and codes the incoming input...

This is a preview of subscription content, log in to check access.


  1. Hyvärinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley Interscience, New YorkGoogle Scholar
  2. Hyvärinen A, Hurri J, Hoyer PO (2009) Natural image statistics. Springer, LondonGoogle Scholar
  3. Simoncelli EP, Olshausen BA (2001) Natural image statistics and neural representation. Annu Rev Neurosci 24:1193–1216PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland