A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks

There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learn...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 29; číslo 11; s. 5394 - 5407
Hlavní autori: Taherkhani, Aboozar, Belatreche, Ammar, Yuhua Li, Maguire, Liam P.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Belief networks
Biofeedback
Brain
Classification
Delay
Delays
Encoding
Feedback
Firing pattern
Learning algorithms
Machine learning
Multilayer neural network
Multilayers
Neural networks
Neurons
Nonhomogeneous media
Spikes
Spiking
spiking neural network (SNN)
Supervised learning
synaptic delay
Synaptic strength
Teaching methods
Training
Weight
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not been investigated thoroughly. This paper proposes a novel biologically plausible supervised learning algorithm for learning precisely timed multiple spikes in a multilayer SNNs. Based on the spike-timing-dependent plasticity learning rule, the proposed learning method trains an SNN through the synergy between weight and delay learning. The weights of the hidden and output neurons are adjusted in parallel. The proposed learning method captures the contribution of synaptic delays to the learning of synaptic weights. Interaction between different layers of the network is realized through biofeedback signals sent by the output neurons. The trained SNN is used for the classification of spatiotemporal input patterns. The proposed learning method also trains the spiking network not to fire spikes at undesired times which contribute to misclassification. Experimental evaluation on benchmark data sets from the UCI machine learning repository shows that the proposed method has comparable results with classical rate-based methods such as deep belief network and the autoencoder models. Moreover, the proposed method can achieve higher classification accuracies than single layer and a similar multilayer SNN.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2018.2797801