From Skin Mechanics to Tactile Neural Coding: Predicting Afferent Neural Dynamics During Active Touch and Perception
First order cutaneous neurons allow object recognition, texture discrimination, and sensorimotor feedback. Their function is well-investigated under passive stimulation while their role during active touch or sensorimotor control is understudied. To understand how human perception and sensorimotor c...
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| Published in: | IEEE transactions on biomedical engineering Vol. 69; no. 12; pp. 3748 - 3759 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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IEEE
01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9294, 1558-2531, 1558-2531 |
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| Abstract | First order cutaneous neurons allow object recognition, texture discrimination, and sensorimotor feedback. Their function is well-investigated under passive stimulation while their role during active touch or sensorimotor control is understudied. To understand how human perception and sensorimotor controlling strategy depend on cutaneous neural signals under active tactile exploration, the finite element (FE) hand and Izhikevich neural dynamic model were combined to predict the cutaneous neural dynamics and the resulting perception during a discrimination test. Using in-vivo microneurography generated single afferent recordings, 75% of the data was applied for the model optimization and another 25% was used for validation. By using this integrated numerical model, the predicted tactile neural signals of the single afferent fibers agreed well with the microneurography test results, achieving the out-of-sample values of 0.94 and 0.82 for slowly adapting type I (SAI) and fast adapting type I unit (FAI) respectively. Similar discriminating capability with the human subject was achieved based on this computational model. Comparable performance with the published numerical model on predicting the cutaneous neural response under passive stimuli was also presented, ensuring the potential applicability of this multi-level numerical model in studying the human tactile sensing mechanisms during active touch. The predicted population-level 1st order afferent neural signals under active touch suggest that different coding strategies might be applied to the afferent neural signals elicited from different cutaneous neurons simultaneously. |
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| AbstractList | First order cutaneous neurons allow object recognition, texture discrimination, and sensorimotor feedback. Their function is well-investigated under passive stimulation while their role during active touch or sensorimotor control is understudied. To understand how human perception and sensorimotor controlling strategy depend on cutaneous neural signals under active tactile exploration, the finite element (FE) hand and Izhikevich neural dynamic model were combined to predict the cutaneous neural dynamics and the resulting perception during a discrimination test. Using in-vivo microneurography generated single afferent recordings, 75% of the data was applied for the model optimization and another 25% was used for validation. By using this integrated numerical model, the predicted tactile neural signals of the single afferent fibers agreed well with the microneurography test results, achieving the out-of-sample values of 0.94 and 0.82 for slowly adapting type I (SAI) and fast adapting type I unit (FAI) respectively. Similar discriminating capability with the human subject was achieved based on this computational model. Comparable performance with the published numerical model on predicting the cutaneous neural response under passive stimuli was also presented, ensuring the potential applicability of this multi-level numerical model in studying the human tactile sensing mechanisms during active touch. The predicted population-level 1st order afferent neural signals under active touch suggest that different coding strategies might be applied to the afferent neural signals elicited from different cutaneous neurons simultaneously.First order cutaneous neurons allow object recognition, texture discrimination, and sensorimotor feedback. Their function is well-investigated under passive stimulation while their role during active touch or sensorimotor control is understudied. To understand how human perception and sensorimotor controlling strategy depend on cutaneous neural signals under active tactile exploration, the finite element (FE) hand and Izhikevich neural dynamic model were combined to predict the cutaneous neural dynamics and the resulting perception during a discrimination test. Using in-vivo microneurography generated single afferent recordings, 75% of the data was applied for the model optimization and another 25% was used for validation. By using this integrated numerical model, the predicted tactile neural signals of the single afferent fibers agreed well with the microneurography test results, achieving the out-of-sample values of 0.94 and 0.82 for slowly adapting type I (SAI) and fast adapting type I unit (FAI) respectively. Similar discriminating capability with the human subject was achieved based on this computational model. Comparable performance with the published numerical model on predicting the cutaneous neural response under passive stimuli was also presented, ensuring the potential applicability of this multi-level numerical model in studying the human tactile sensing mechanisms during active touch. The predicted population-level 1st order afferent neural signals under active touch suggest that different coding strategies might be applied to the afferent neural signals elicited from different cutaneous neurons simultaneously. First order cutaneous neurons allow object recognition, texture discrimination, and sensorimotor feedback. Their function is well-investigated under passive stimulation while their role during active touch or sensorimotor control is understudied. To understand how human perception and sensorimotor controlling strategy depend on cutaneous neural signals under active tactile exploration, the finite element (FE) hand and Izhikevich neural dynamic model were combined to predict the cutaneous neural dynamics and the resulting perception during a discrimination test. Using in-vivo microneurography generated single afferent recordings, 75% of the data was applied for the model optimization and another 25% was used for validation. By using this integrated numerical model, the predicted tactile neural signals of the single afferent fibers agreed well with the microneurography test results, achieving the out-of-sample values of 0.94 and 0.82 for slowly adapting type I (SAI) and fast adapting type I unit (FAI) respectively. Similar discriminating capability with the human subject was achieved based on this computational model. Comparable performance with the published numerical model on predicting the cutaneous neural response under passive stimuli was also presented, ensuring the potential applicability of this multi-level numerical model in studying the human tactile sensing mechanisms during active touch. The predicted population-level 1st order afferent neural signals under active touch suggest that different coding strategies might be applied to the afferent neural signals elicited from different cutaneous neurons simultaneously. |
| Author | Makdani, Adarsh Marshall, Andrew G Zou, Zhenmin McGlone, Francis P Ren, Lei Wei, Yuyang Wei, Guowu |
| Author_xml | – sequence: 1 givenname: Yuyang orcidid: 0000-0002-3200-8598 surname: Wei fullname: Wei, Yuyang organization: Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, U.K – sequence: 2 givenname: Francis P orcidid: 0000-0002-0881-635X surname: McGlone fullname: McGlone, Francis P organization: School of Natural Sciences and Psychology, Liverpool John Moores University, U.K – sequence: 3 givenname: Andrew G orcidid: 0000-0001-8273-7089 surname: Marshall fullname: Marshall, Andrew G organization: Institute of Aging and Chronic Disease, University of Liverpool, U.K – sequence: 4 givenname: Adarsh surname: Makdani fullname: Makdani, Adarsh organization: School of Natural Sciences and Psychology, Liverpool John Moores University, U.K – sequence: 5 givenname: Zhenmin surname: Zou fullname: Zou, Zhenmin organization: Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, U.K – sequence: 6 givenname: Lei orcidid: 0000-0003-3222-2102 surname: Ren fullname: Ren, Lei email: lei.ren@manchester.ac.uk organization: Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester, U.K – sequence: 7 givenname: Guowu orcidid: 0000-0003-2613-902X surname: Wei fullname: Wei, Guowu email: g.wei@salford.ac.uk organization: School of Science, Engineering and Environment, University of Salford, Manchester, U.K |
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| SubjectTerms | Active control active touch Coding Computational modeling Computational neuroscience Dynamic models FE Human hand Fibers Humans Mathematical models Mechanoreceptors - physiology Microneurography Neural coding Neurons Neurons, Afferent - physiology Neurophysiological Neurophysiology Numerical models Numerical prediction Object recognition Optimization Pattern recognition Perception Sensorimotor system Sensory neurons Skin skin mechanics Tactile discrimination Tactile stimuli Texture recognition Touch Touch - physiology Touch Perception |
| Title | From Skin Mechanics to Tactile Neural Coding: Predicting Afferent Neural Dynamics During Active Touch and Perception |
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