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Region-based encoding method using multi-dimensional Gaussians for networks of spiking neurons
, Lakshmi Narayana Panuku
Published in IEEE
2008
Volume: 4984 LNCS
   
Issue: PART 1
Pages: 73 - 82
Abstract
In this paper, we address the issues in representation of continuous valued variables by firing times of neurons in the spiking neural network used for clustering multi-variate data. The existing range-based encoding method encodes each dimension separately. This method does not make use of the correlation among the different variables, and the knowledge of the distribution of data. We propose a region-based encoding method that places multi-dimensional Gaussian receptive fields in the data-inhabited regions, and captures the correlation among the variables. Effectiveness of the proposed encoding method in clustering the complex 2-dimensional and 3-dimensional data sets is demonstrated. © 2008 Springer-Verlag Berlin Heidelberg.
About the journal
JournalData powered by TypesetLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherData powered by TypesetIEEE
ISSN03029743
Open AccessNo
Concepts (21)
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    Correlation methods
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    Encoding (symbols)
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    FLOW OF SOLIDS
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    Image segmentation
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    Network protocols
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    Neural networks
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    Neurons
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    Sensor networks
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    Clustering
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    CONTINUOUS
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    DIMENSIONAL DATUM
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    DISTRIBUTION OF DATUM
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    ENCODING METHODS
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    FIRING TIMES
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    Gaussian
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    Gaussians
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    MULTI-DIMENSIONAL
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    RECEPTIVE FIELDS
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    Spiking neural networks
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    SPIKING NEURONS
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    Knowledge based systems