Algorithms for clustering data
Algorithms for clustering data
Fast sigmoidal networks via spiking neurons
Neural Computation
Pulsed Neural Networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
IEEE Transactions on Neural Networks
Region-Based Encoding Method Using Multi-dimensional Gaussians for Networks of Spiking Neurons
Neural Information Processing
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In this paper, we study the clustering capabilities of spiking neural networks. We first study the working of spiking neural networks for clustering linearly separable data. Also, a biological interpretation has been given to the delay selection in spiking neural networks. We show that by varying the firing threshold of spiking neurons during the training, nonlinearly separable data like the ring data can be clustered. When a multi-layer spiking neural network is trained for clustering, subclusters are formed in the hidden layer and these subclusters are combined in the output layer, resulting in hierarchical clustering of the data. A spiking neural network with a hidden layer is generally trained by modifying the weights of the connections to the nodes in the hidden layer and the output layer simultaneously. We propose a two-stage learning method for training a spiking neural network model for clustering. In the proposed method, the weights for the connections to the nodes in the hidden layer are learnt first, and then the weights for the connections to the nodes in the output layer are learnt. We show that the proposed two-stage learning method can cluster complex data such as the interlocking cluster data, without using lateral connections.