Fast sigmoidal networks via spiking neurons
Neural Computation
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pulsed Neural Networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Unsupervised Learning in Networks of Spiking Neurons Using Temporal Coding
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Isolated word recognition with the Liquid State Machine: a case study
Information Processing Letters - Special issue on applications of spiking neural networks
Clustering of nonlinearly separable data using spiking neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
IEEE Transactions on Neural Networks
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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.