The nature of statistical learning theory
The nature of statistical learning theory
Reducing Communication for Distributed Learning in Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Sphinx-4: a flexible open source framework for speech recognition
Sphinx-4: a flexible open source framework for speech recognition
CiE '07 Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World
Region-Based Encoding Method Using Multi-dimensional Gaussians for Networks of Spiking Neurons
Neural Information Processing
Photonic Reservoir Computing with Coupled Semiconductor Optical Amplifiers
OSC '08 Proceedings of the 1st international workshop on Optical SuperComputing
International Journal of Applied Mathematics and Computer Science - Special Section: Selected Topics in Biological Cybernetics, Special Editors: Andrzej Kasiński and Filip Ponulak
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Artificial Intelligence in Medicine
Accelerating event based simulation for multi-synapse spiking neural networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
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The Liquid State Machine (LSM) is a recently developed computational model with interesting properties. It can be used for pattern classification, function approximation and other complex tasks. Contrary to most common computational models, the LSM does not require information to be stored in some stable state of the system: the inherent dynamics of the system are used by a memoryless readout function to compute the output. In this paper we present a case study of the performance of the Liquid State Machine based on a recurrent spiking neural network by applying it to a well known and well studied problem: speech recognition of isolated digits. We evaluate different ways of coding the speech into spike trains. In its optimal configuration, the performance of the LSM approximates that of a state-of-the-art recognition system. Another interesting conclusion is the fact that the biologically most realistic encoding performs far better than more conventional methods.