Supervised Learning in Multilayer Spiking Neural Networks

Abstract

The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

arXiv comment38 pages, 4 figures

References in corpus (2)

• Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding
• Fractionally Predictive Spiking Neurons

Cited by in corpus (23)

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• A Unified Platform to Evaluate STDP Learning Rule and Synapse Model using Pattern Recognition in a Spiking Neural Network
• Linear Constraints Learning for Spiking Neurons
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• BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks
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