Finding iterative roots with a spiking neural network
Information Processing Letters - Special issue on applications of spiking neural networks
Region-Based Encoding Method Using Multi-dimensional Gaussians for Networks of Spiking Neurons
Neural Information Processing
Finding iterative roots with a spiking neural network
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
Text-independent speaker authentication with spiking neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Segmentation and Edge Detection Based on Spiking Neural Network Model
Neural Processing Letters
Cell microscopic segmentation with spiking neuron networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
A temporal coding hardware implementation for spiking neural networks
ACM SIGARCH Computer Architecture News
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
An application of pattern recognition based on optimized RBF-DDA neural networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Learning algorithm for spiking neural networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Sound classification and function approximation using spiking neural networks
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Perceptive, non-linear speech processing and spiking neural networks
Nonlinear Speech Modeling and Applications
Spike-timing-dependent construction
Neural Computation
Evolving spiking wavelet-neuro-fuzzy self-learning system
Applied Soft Computing
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We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multilayer network can induce hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an efficient use of neurons: input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale-sensitivity of the network and show how the induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters