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Efficient single input-output layer spiking neural classifier with time-varying weight model
The performance of SEF-M is evaluated against state-of-the-art spiking neural network learning algorithms on 10 benchmark datasets from UCI machine learning repository.
A novel method for extracting interpretable knowledge from a spiking neural classifier with time-varying synaptic weights
A set of FSFs corresponding to each output class represents the extracted knowledge from the classifier.
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