title: Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.
abstract: We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.
authors.affiliation: Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, United Kingdom.
authors.forename: Yoonsik
authors.identifier: http://orcid.org/0000-0002-0883-7150
authors.initials: Y
authors.lastname: Shim
authors.affiliation: Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, United Kingdom.
authors.forename: Andrew
authors.identifier: http://orcid.org/0000-0001-5503-0467
authors.initials: A
authors.lastname: Philippides
authors.affiliation: Neuroscience, School of Life Sciences, University of Sussex, Falmer, Brighton, United Kingdom.
authors.forename: Kevin
authors.initials: K
authors.lastname: Staras
authors.affiliation: Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, United Kingdom.
authors.forename: Phil
authors.identifier: http://orcid.org/0000-0002-5164-4004
authors.initials: P
authors.lastname: Husbands
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001683:Biological Clocks / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods; D046709:Pattern Recognition, Physiological / Q000502:physiology*; D000069558:Unsupervised Machine Learning