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.
Similar content being viewed by others
References
Johansson RS, Flanagan JR (2009) Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat Rev Neurosci 10(5):345
Libouton X, Barbier O, Berger Y, Plaghki L, Thonnard JL (2012) Tactile roughness discrimination of the finger pad relies primarily on vibration sensitive afferents not necessarily located in the hand. Behav Brain Res 229(1):273–279
Vallbo A, Johansson RS et al (1984) Properties of cutaneous mechanoreceptors in the human hand related to touch sensation. Hum Neurobiol 3(1):3–14
Lesniak DR, Wellnitz SA, Gerling GJ, Lumpkin EA (2009) Statistical analysis and modeling of variance in the sa-i mechanoreceptor response to sustained indentation. In: Engineering in medicine and biology society, 2009. EMBC 2009. Annual international conference of the IEEE. IEEE, pp 6814–6817
Tee BCK, Chortos A, Berndt A, Nguyen AK, Tom A, McGuire A, Lin ZC, Tien K, Bae WG, Wang H et al (2015) A skin-inspired organic digital mechanoreceptor. Science 350(6258):313–316
Kawasaki H, Komatsu T, Uchiyama K (2002) Dexterous anthropomorphic robot hand with distributed tactile sensor: Gifu hand II. IEEE/ASME Trans Mechatron 7(3):296–303
Chen H, Miao L, Su Z, Song Y, Han M, Chen X, Cheng X, Chen D, Zhang H (2017) Fingertip-inspired electronic skin based on triboelectric sliding sensing and porous piezoresistive pressure detection. Nano Energy 40:65–72
Hughes D, Correll N (2015) Texture recognition and localization in amorphous robotic skin. Bioinspiration Biomimetics 10(5):055002
Wandersman E, Candelier R, Debrégeas G, Prevost A (2011) Texture-induced modulations of friction force: the fingerprint effect. Phys Rev Lett 107(6):164301
Scheibert J, Leurent S, Prevost A, Debrégeas G (2009) The role of fingerprints in the coding of tactile information probed with a biomimetic sensor. Science 323(5920):1503–1506
Liu Y, Bao R, Tao J, Li J, Dong M, Pan C (2020) Recent progress in tactile sensors and their applications in intelligent systems. Sci Bull 65(1):70–88
Chathuranga DS, Hirai S, et al. (2013) Investigation of a biomimetic fingertip’s ability to discriminate fabrics based on surface textures. In: 2013 IEEE/ASME international conference on advanced intelligent mechatronics. IEEE, pp 1667–1674
Feng J, Jiang Q (2019) Slip and roughness detection of robotic fingertip based on FBG. Sens Actuat A 287:143–149
Salehi S, Cabibihan JJ, Ge SS (2011) Artificial skin ridges enhance local tactile shape discrimination. Sensors 11(9):8626–8642
Dayan P, Abbott LF (2001) Theoretical neuroscience: computational and mathematical modeling of neural systems. Computational neuroscience series. Massachusetts Institute of Technology Press
Bassett DS, Khambhati AN, Grafton ST (2017) Emerging frontiers of neuroengineering: a network science of brain connectivity. Annu Rev Biomed Eng 19:327–352
Ghosh-Dastidar S, Adeli H (2009) Spiking neural networks. Int J Neural Syst 19(04):295–308
Lin P, Chang S, Wang H, Huang Q, He J (2019) Spikecd: a parameter-insensitive spiking neural network with clustering degeneracy strategy. Neural Comput Appl 31(8):3933–3945
Xie X, Wen S, Yan Z, Huang T, Chen Y (2020) Designing pulse-coupled neural networks with spike-synchronization-dependent plasticity rule: image segmentation and memristor circuit application. Neural Comput Appl 32(17):13441–13452
Chen J, Li K, Rong H, Bilal K, Li K, Yu PS (2019) A periodicity-based parallel time series prediction algorithm in cloud computing environments. Inf Sci 496:506–537
Zhang L, Li K, Li C, Li K (2017) Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems. Inf Sci 379:241–256
Chicharro D, Kreuz T, Andrzejak RG (2011) What can spike train distances tell us about the neural code? J Neurosci Methods 199(1):146–165
Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F (2012) Monitoring spike train synchrony. J Neurophysiol 109(5):1457–1472
Chen J, Yu P (2019) A domain adaptive density clustering algorithm for data with varying density distribution. IEEE Trans Knowl Data Eng 1–1
Kreuz T, Mulansky M, Bozanic N (2015) Spiky: a graphical user interface for monitoring spike train synchrony. J Neurophysiol 113(9):3432–3445
Rongala UB, Mazzoni A, Oddo CM (2017) Neuromorphic artificial touch for categorization of naturalistic textures. IEEE Trans Neural Network Learn Syst 28(4):819–829
Zhengkun Y, Yilei Z (2017) Recognizing tactile surface roughness with a biomimetic fingertip: a soft neuromorphic approach. Neurocomputing 244:102–111
Bologna L, Pinoteau J, Passot J, Garrido J, Vogel J, Vidal ER, Arleo A (2013) A closed-loop neurobotic system for fine touch sensing. J Neural Eng 10(4):046019
Oddo CM, Controzzi M, Beccai L, Cipriani C, Carrozza MC (2011) Roughness encoding for discrimination of surfaces in artificial active-touch. IEEE Trans Rob 27(3):522–533
Song A, Han Y, Hu H, Li J (2014) A novel texture sensor for fabric texture measurement and classification. IEEE Trans Instrum Meas 63(7):1739–1747
Nakamoto H, Matsumoto T (2016) Tactile texture classification using magnetic tactile sensor. Int J Appl Electromagnet Mech 52(3–4):1673–1679
Jamali N, Sammut C (2011) Majority voting: material classification by tactile sensing using surface texture. IEEE Trans Rob 27(3):508–521
Qin L, Yi Z, Zhang Y (2017) Enhanced surface roughness discrimination with optimized features from bio-inspired tactile sensor. Sens Actuat A 264:133–140
Nazari S, Faez K, Amiri M, Karami E (2015) A digital implementation of neuron-astrocyte interaction for neuromorphic applications. Neural Netw 66:79–90
Walter F, Röhrbein F, Knoll A (2015) Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks. Neural Netw 72:152–167
Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117(4):500–544
Stein RB (1967) Some models of neuronal variability. Biophys J 7(1):37–68
Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14(6):1569–1572
Eliasmith C, Anderson CH (2004) Neural engineering: computation, representation, and dynamics in neurobiological systems. MIT Press, Cambridge
Lee WW, Yu H, Thakor NV (2014) Gait event detection through neuromorphic spike sequence learning. In: 2014 5th IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics. IEEE, pp 899–904
Friedl KE, Voelker AR, Peer A, Eliasmith C (2016) Human-inspired neurorobotic system for classifying surface textures by touch. IEEE Robot Autom Lett 1(1):516–523
Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC et al (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23(3):349–398
Izhikevich EM (2004) Which model to use for cortical spiking neurons? IEEE Trans Neural Netw 15(5):1063–1070
Misra J, Saha I (2010) Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74(1–3):239–255
Barron-Zambrano JH, Torres-Huitzil C (2013) Fpga implementation of a configurable neuromorphic cpg-based locomotion controller. Neural Netw 45:50–61
Cunningham JP, Gilja V, Ryu SI, Shenoy KV (2009) Methods for estimating neural firing rates, and their application to brain–machine interfaces. Neural Netw 22(9):1235–1246
Ostojic S (2011) Interspike interval distributions of spiking neurons driven by fluctuating inputs. J Neurophysiol 106(1):361–373
Lestienne R (2001) Spike timing, synchronization and information processing on the sensory side of the central nervous system. Prog Neurobiol 65(6):545–591
Johansson RS, Birznieks I (2004) First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nat Neurosci 7(2):170
Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324
Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303
Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4–5):411–430
Deza MM, Deza E (2009) Encyclopedia of distances. In: Encyclopedia of distances. Springer
Kreuz T, Haas JS, Morelli A, Abarbanel HD, Politi A (2007) Measuring spike train synchrony. J Neurosci Methods 165(1):151–161
Kreuz T, Chicharro D, Greschner M, Andrzejak RG (2011) Time-resolved and time-scale adaptive measures of spike train synchrony. J Neurosci Methods 195(1):92–106
Quiroga RQ, Kreuz T, Grassberger P (2002) Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Phys Rev E 66(4):041904
Rossum M (2001) A novel spike distance. Neural Comput 13(4):751–763
Victor JD, Purpura KP (1996) Nature and precision of temporal coding in visual cortex: a metric-space analysis. J Neurophysiol 76(2):1310–1326
Yi Z, Zhang Y, Peters J (2017) Bioinspired tactile sensor for surface roughness discrimination. Sens Actuat A 255:46–53
Qin L, Zhang Y (2018) Roughness discrimination with bio-inspired tactile sensor manually sliding on polished surfaces. Sens Actuat A 279:433–441
Acknowledgements
Longhui Qin would like to thank the support of the Fundamental Research Funds for the Central Universities (No. 2242021R10024).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Qin, L., Zhang, Y. A reference spike train-based neurocomputing method for enhanced tactile discrimination of surface roughness. Neural Comput & Applic 33, 14793–14807 (2021). https://doi.org/10.1007/s00521-021-06119-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-06119-y