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
Improving reservoirs using intrinsic plasticity
Neurocomputing
Continuous speech recognition with sparse coding
Computer Speech and Language
Pruning and regularization in reservoir computing
Neurocomputing
Improving the separability of a reservoir facilitates learning transfer
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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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.