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Dynamic cluster formation using populations of spiking neurons

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Belatreche, Ammar and Paul, Rakesh (2012) Dynamic cluster formation using populations of spiking neurons. In: The 2012 IEEE International Joint Conference on Neural Networks (IJCNN), , Brisbane, Australia. IEEE. 6 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6252532&isnumber=6252360

DOI: 10.1109/IJCNN.2012.6252532

Abstract

This paper introduces a novel neuro-dynamic system for adaptive online clustering using populations of spiking neurons and spike-timing dependent plasticity (STDP). Real-valued data samples are temporally encoded into spike events, used by biological neurons to encode information and communicate with one another, and clusters are represented by spiking neuron populations of varying size. The number of clusters is unknown a priori and clusters are learned in an online fashion where each data sample is provided only once. The coincidence detection capability of spiking neurons is utilized for data clustering and clusters are dynamically formed. The structure of the spiking neural network is constantly adjusted through adding and pruning of neuron populations. Besides, the number of neurons within each population constantly adapts as new data arrives. STDP is employed to adjust the strength of synaptic connections and enhance the selectivity of each population to its corresponding group of data. Preliminary experiments were carried out on synthetic and selected benchmark datasets to evaluate the performance of the proposed system. Promising results were obtained, which indicate the viability of spike-based population coding for online data clustering.

Item Type:Conference contribution (Paper)
Keywords:Spiking Neurons, Unsupervised Learning, Online Clustering, Population Coding, STDP, Spike Response Model
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:23629
Deposited By:Dr Ammar Belatreche
Deposited On:23 Oct 2012 10:52
Last Modified:23 Oct 2012 10:52

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