Clustering of Nonlinearly Separable Data Using Spiking Neural Networks

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.