Key-Threshold based spiking neural network | IEEE Conference Publication | IEEE Xplore

Key-Threshold based spiking neural network


Abstract:

In the paper a novel model of Key-Threshold based Spiking Neural Network (KTSNN) is proposed. This neural network consists of quasi-neurons oriented to recognize any key-...Show More

Abstract:

In the paper a novel model of Key-Threshold based Spiking Neural Network (KTSNN) is proposed. This neural network consists of quasi-neurons oriented to recognize any key-spikes distributed in time (sequence of spikes) or in space (in synapses). Every neuron aims to recognize key (template of spikes) stored in its memory and to react by output spike at successfully detection. Software implementation of this model is suggested. Possible methods of learning, implementation and usage of this model are discussed.
Date of Conference: 25-29 September 2017
Date Added to IEEE Xplore: 07 December 2017
ISBN Information:
Conference Location: Vladivostok, Russia

I. Introduction

Last decade we watch a tremendous rise of neural networks and its applications focusing on and inspired by deep [1] and spiking neural networks [2], [3]. The reason of this trend is achievements in hardware technology allowing design and usage of large neural networks.

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References

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