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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 21/2021

29.06.2021 | Original Article

A reference spike train-based neurocomputing method for enhanced tactile discrimination of surface roughness

verfasst von: Longhui Qin, Yilei Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 21/2021

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Abstract

Spike trains (STs) induced by external stimuli are complex and challenging to decode the embedded spatiotemporal information. Although various methods have been developed to characterize STs, few applications have been reported in tactile discrimination applications. In this paper, a neurocomputing method based on reference spike train (RST) is proposed to establish a neural computation scheme, on which existing ST metrics could be fed into various traditional models directly. Moreover, existing metrics in the field of statistics and vector measurement are introduced together to extract more discriminative features. With the binning technique and feature selection algorithm applied, the neural computation scheme is aimed at taking advantage of as maximal as possible information contained in tactile signals. Based on our designed artificial fingertip, the effect is validated by improving the recognition accuracy from 77.6% to 83.4% when it is applied to the discrimination of eight roughness surfaces. Furthermore, properties of RST, such as spike intervals and distributions, are evaluated and it is found that RSTs with uniform distribution perform the best for tactile discrimination.
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Metadaten
Titel
A reference spike train-based neurocomputing method for enhanced tactile discrimination of surface roughness
verfasst von
Longhui Qin
Yilei Zhang
Publikationsdatum
29.06.2021
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 21/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-06119-y

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