Computing with spiking neurons
Pulsed neural networks
Pulsed Neural Networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
Movement prediction from real-world images using a liquid state machine
Applied Intelligence
Neural Computation
CiE '07 Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
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Liquid state machine is a recent concept whose aptitude for spatiotemporal pattern recognition tasks has already been demonstrated. It consists in stimulating an untrained spiking neural network with input streams, creating complex dynamics that form the liquid state. An external function, the readout, is trained to map the liquid states into the desired outputs. In this paper, different avenues are explored to improve the classification performance of the readout. First are compared the membrane potentials and the firing rates of the neurons as two different liquid state representations. We also propose a new liquid state representation based on the frequency components of short-time membrane potential signals. Tests on synthetic and real data reveal that the frequency-based representation gets higher recognition rates than by using membrane potential or firing rates. Finally, we show that the combination of the different liquid states can improve the classification performance on spatiotemporal data.