An unsupervised neuromorphic clustering algorithm

Alan Diamond, Michael Schmuker, Thomas Nowotny

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
51 Downloads (Pure)


Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, highly parallel operation of brains. However, to use these systems in applications, we need “neuromorphic algorithms” that can run on them. Here we develop a spiking neural network model for neuromorphic hardware that uses spike timing-dependent plasticity and lateral inhibition to perform unsupervised clustering. With this model, time-invariant, rate-coded datasets can be mapped into a feature space with a specified resolution, i.e., number of clusters, using exclusively neuromorphic hardware. We developed and tested implementations on the SpiNNaker neuromorphic system and on GPUs using the GeNN framework. We show that our neuromorphic clustering algorithm achieves results comparable to those of conventional clustering algorithms such as self-organizing maps, neural gas or k-means clustering. We then combine it with a previously reported supervised neuromorphic classifier network to demonstrate its practical use as a neuromorphic preprocessing module.

Original languageEnglish
Pages (from-to)423-437
Number of pages15
JournalBiological Cybernetics
Issue number4
Early online date3 Apr 2019
Publication statusPublished - 1 Aug 2019


  • Classification
  • Data clustering
  • Neuromorphic hardware
  • Self-organizing map
  • Spiking neural networks
  • Unsupervised learning


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