Processing images by semi-linear predictability minimization

Nicol N. Schraudolph, Martin Eldracher, Jürgen Schmidhuber

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


In the predictability minimization approach, input patterns are fed into a system consisting of adaptive, initially unstructured feature detectors. There are also adaptive predictors constantly trying to predict current feature detector outputs from other feature detector outputs. Simultaneously, however, the feature detectors try to become as unpredictable as possible, resulting in a co-evolution of predictors and feature detectors. This paper describes the implementation of a visual processing system trained by semi-linear predictability minimization, and presents many experiments that examine its response to artificial and real-world images. In particular, we observe that under a wide variety of conditions, predictability minimization results in the development of well-known visual feature detectors.
Original languageEnglish (US)
Pages (from-to)133-169
Number of pages37
JournalNetwork: Computation in Neural Systems
Issue number2
StatePublished - Jan 1 1999
Externally publishedYes

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)


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