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

• Published in: Neural computing & applications Vol. 33; no. 21; pp. 14793 - 14807
• Main Authors: Qin, Longhui, Zhang, Yilei
• Format: Journal Article
• Language: English
• Published: London Springer London 01-11-2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Clustering; Computation; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Discrimination; Feature extraction; Feature selection; Image Processing and Computer Vision; Machine learning; Mechanical engineering; Microelectromechanical systems; Nervous system; Neural networks; Neurons; Original Article; Probability and Statistics in Computer Science; Sensors; Signal processing; Support vector machines; Surface roughness; Tactile discrimination; Neuromorphic scheme; Tactile signal processing; Feature extraction; Machine learning
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06119-y

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.