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Modeling and Invariance

  • Mario NegrelloEmail author
Chapter
  • 472 Downloads
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 1)

Abstract

Further in the analysis of invariants and their explanatory roles, this chapter introduces models of computational neuroscience that elucidate the interplay between constancy and variability, and illustrate the appearance of invariances. These models rely on the interplay between the structure of the input and rules of structural modification, in which a malleable structure organizes by assimilating the regularities in input. In analogy, organisms learning and adapting to their bodies and environments also self-organize function. Although powerful, empirical assessment of invariances and models thereof present partial pictures. To assemble this mosaic of models, it is necessary to have a theory that is able to cover the causal levels of behavioral phenomena. Dynamical systems is proposed as such a theory.

Keywords

Recurrent Neural Network Place Cell Hebbian Learning Spiral Pattern Computational Neuroscience 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Okinawa Institute of Science and TechnologyOkinawaJapan

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