BMC Neuroscience

, 14:P290 | Cite as

Classification of multivariate data with a spiking neural network on neuromorphic hardware

  • Michael Schmuker
  • Thomas Pfeil
  • Martin P Nawrot
Open Access
Poster presentation


Neural Network Lateral Inhibition Multivariate Data Olfactory System Synaptic Weight 
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.

Progress in the field of computational systems neuroscience has uncovered a number of computational principles employed by nervous systems. At the same time neuromorphic hardware systems have evolved to a state where fast in silico implementations of complex neural networks are feasible. The current challenge is to identify and implement functional neural networks that enable neuromorphic computing to solve real world problems. Here, we present a generic spiking neural network for the supervised classification of multivariate data, a common problem in signal and data analysis. The network architecture was inspired by the data processing scheme of the olfactory system [1]. It has a three stage architecture. In the first stage, real-valued multivariate data is encoded into a bounded, positive firing-rate representation. The second stage removes correlation between input channels through lateral inhibition. Supervised training affects synaptic weights in the third stage, where classification of input patterns is performed.

We implemented and tested our network on the Spikey neuromorphic hardware system [2]. Our network performed on the same level as a Naïve Bayes classifier on several benchmark data sets. Our classifier network is an important proof-of-principle for a bio-inspired functional spiking network implemented on neuromorphic hardware performing a real-world computing task.

This study was funded by the DFG (SCHM2474/1-1, MS), the BMBF (01GQ1001D, MS and MN, 01GQ0941, MN) and the European Union (243914, TP).


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    Schmuker M, Schneider G: Processing and classification of chemical data inspired by insect olfaction. PNAS. 2007, 104: 20285-9. 10.1073/pnas.0705683104.PubMedCentralCrossRefPubMedGoogle Scholar
  2. 2.
    Pfeil T, Grübl A, Jeltsch S, Müller E, Müller P, Petrovici MA, Schmuker M, Brüderle D, Schemmel J, Meier K: Six networks on a universal neuromorphic computing substrate. Frontiers Neuromorph Eng. 2013, 7: 11-Google Scholar

Copyright information

© Schmuker et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Michael Schmuker
    • 1
    • 2
  • Thomas Pfeil
    • 3
  • Martin P Nawrot
    • 1
    • 2
  1. 1.Theoretical NeuroscienceInstitute of Biology, Freie Universität BerlinBerlinGermany
  2. 2.Bernstein Center for Computational Neuroscience BerlinBerlinGermany
  3. 3.Kirchhoff-Institute for PhysicsHeidelberg UniversityHeidelbergGermany

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