From ANN to Biomimetic Information Processing

  • Anders Lansner
  • Simon Benjaminsson
  • Christopher Johansson
Part of the Studies in Computational Intelligence book series (SCI, volume 188)


Artificial neural networks (ANN) are useful components in today’s data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits.


Support Vector Machine Artificial Neural Network Olfactory Bulb Hybrid Algorithm Handwritten Digit 
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-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Anders Lansner
    • 1
    • 2
  • Simon Benjaminsson
    • 1
  • Christopher Johansson
    • 1
  1. 1.Dept Computational BiologyRoyal Institute of Technology (KTH) 
  2. 2.Dept Numerical Analysis and Computer Science, AlbaNova University CenterStockholm UniversityStockholm 

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