Hebbian Learning Clustering with Rulkov Neurons

  • Jenny Held
  • Tom Lorimer
  • Carlo Albert
  • Ruedi StoopEmail author
Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 191)


The recent explosion of high dimensional, high resolution ‘big-data’ from automated bioinformatics measurement techniques demands new methods for unsupervised data processing. An essential analysis step is the identification of groups of similar data, or ‘clusters’, in noisy high-dimensional data spaces, as this permits to perform some analysis steps at the group level. Popular clustering algorithms introduce an undesired cluster shape bias, require prior knowledge of the number of clusters, and are unable to properly deal with noise. Manual data gating, often used to assist these methods, is based on low-dimensional projection techniques, which is prone to obscure the underlying data structure. While Hebbian Learning Clustering successfully overcomes all of these limitations (by using only local similarities to infer global structure), previous implementations were unsuited to deal with big data sets. Here, we present a novel implementation based on realistic neuronal dynamics that removes also this obstacle. By a performance that scales favourably compared to all standard clustering algorithms, unbiased large data analysis becomes feasible on standard desktop hardware.


Coupling Strength Data Item Hebbian Learn Human Bone Marrow Cell Average Synchrony 
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 International Publishing AG 2017

Authors and Affiliations

  • Jenny Held
    • 1
    • 2
    • 3
  • Tom Lorimer
    • 2
    • 3
  • Carlo Albert
    • 1
  • Ruedi Stoop
    • 2
    • 3
    Email author
  1. 1.EawagSwiss Federal Institute of Aquatic Science and TechnologyDübendorfSwitzerland
  2. 2.Institute of Neuroinformatics and Institute for Computational ScienceUniversity of ZürichZurichSwitzerland
  3. 3.ETH ZürichZurichSwitzerland

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