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2019-03-22-Diamondetal-BiolCybern.pdf (3.66 MB)

An unsupervised neuromorphic clustering algorithm

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posted on 2023-06-09, 17:30 authored by Alan Diamand, Michael SchmukerMichael Schmuker, Thomas NowotnyThomas Nowotny
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.

Funding

Biomachinelearning: Bio-inspired Machine Learning for Chemical Sensing (fellow: Michael Schmuker); G1382; EUROPEAN UNION; PIEF-GA-2012-331892

Human Brian Project Specific Grant Agreement 2; G2410; EUROPEAN UNION; 785907

Human Brain Project: Neuromorphic Implementations of Multivariate Classification Inspired by the Olfactory System; G1359; EUROPEAN UNION; 604102 HBP NEUROCLASSIOS

Odor-background segregation and source localization using fast olfactory processing; G1652; HUMAN FRONTIER SCIENCE PROGRAM (HFSP); RGP0053/2015

Human Brain Project Specific Grant Agreement 1 - HBP SGA1; G1972; EUROPEAN UNION; 72027

History

Publication status

  • Published

File Version

  • Published version

Journal

Biological Cybernetics

ISSN

0340-1200

Publisher

Springer

Department affiliated with

  • Informatics Publications

Research groups affiliated with

  • Centre for Computational Neuroscience and Robotics Publications
  • Evolutionary and Adaptive Systems Research Group Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2019-04-05

First Open Access (FOA) Date

2019-04-05

First Compliant Deposit (FCD) Date

2019-04-04

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