title: Hebbian self-organizing integrate-and-fire networks for data clustering.
abstract: We propose a Hebbian learning-based data clustering algorithm using spiking neurons. The algorithm is capable of distinguishing between clusters and noisy background data and finds an arbitrary number of clusters of arbitrary shape. These properties render the approach particularly useful for visual scene segmentation into arbitrarily shaped homogeneous regions. We present several application examples, and in order to highlight the advantages and the weaknesses of our method, we systematically compare the results with those from standard methods such as the k-means and Ward's linkage clustering. The analysis demonstrates that not only the clustering ability of the proposed algorithm is more powerful than those of the two concurrent methods, the time complexity of the method is also more modest than that of its generally used strongest competitor.
authors.affiliation: Institute of Neuroinformatics, University of Zurich and ETH Zurich, CH-8057, Zurich, Switzerland. flandisf@ethz.ch
authors.forename: Florian
authors.initials: F
authors.lastname: Landis
authors.forename: Thomas
authors.initials: T
authors.lastname: Ott
authors.forename: Ruedi
authors.initials: R
authors.lastname: Stoop
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D002490:Central Nervous System / Q000502:physiology*; D016000:Cluster Analysis; D003198:Computer Simulation; D008432:Mathematical Computing; D055641:Mathematical Concepts; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D009435:Synaptic Transmission / Q000502:physiology