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A Supervised Multi-Spike Learning Algorithm for Spiking Neural Networks | IEEE Conference Publication | IEEE Xplore

A Supervised Multi-Spike Learning Algorithm for Spiking Neural Networks


Abstract:

The formulation of efficient supervised learning algorithms for Spiking Neural Network (SNN) is difficult and remains challenging. This paper presents a supervised multis...Show More

Abstract:

The formulation of efficient supervised learning algorithms for Spiking Neural Network (SNN) is difficult and remains challenging. This paper presents a supervised multispike learning algorithm, which is used to train neurons to output spike train with a target firing rate. The proposed algorithm simplifies the expression of the membrane potential by assuming a special condition of the threshold, thus allows the application of a gradient descent to optimize the synaptic weights. Additionally, in the presented experimental results, the proposed algorithm is evaluated regarding its initial setups, its classification performance for rate-based and timing-based patterns and its capability to sound recognition. The results also demonstrate that the proposed algorithm can achieve a competitive accuracy in temporal pattern classification and sound recognition.
Date of Conference: 08-13 July 2018
Date Added to IEEE Xplore: 14 October 2018
ISBN Information:
Electronic ISSN: 2161-4407
Conference Location: Rio de Janeiro, Brazil

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