Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator
The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-05-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00070/full |
| _version_ | 1818972023974526976 |
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| author | Thomas eHoellinger Mathieu ePetieau Matthieu eDuvinage Thierry eCastermans Karthik eSeetharaman Ana-Maria eCebolla Ana eBengoetxea Yuri P Ivanenko Bernard eDan Guy eCheron Guy eCheron |
| author_facet | Thomas eHoellinger Mathieu ePetieau Matthieu eDuvinage Thierry eCastermans Karthik eSeetharaman Ana-Maria eCebolla Ana eBengoetxea Yuri P Ivanenko Bernard eDan Guy eCheron Guy eCheron |
| author_sort | Thomas eHoellinger |
| collection | DOAJ |
| description | The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications. |
| first_indexed | 2024-12-20T15:01:41Z |
| format | Article |
| id | doaj.art-bad94097210042cca860b0aa9f03df34 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-20T15:01:41Z |
| publishDate | 2013-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-bad94097210042cca860b0aa9f03df342022-12-21T19:36:38ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882013-05-01710.3389/fncom.2013.0007043150Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generatorThomas eHoellinger0Mathieu ePetieau1Matthieu eDuvinage2Thierry eCastermans3Karthik eSeetharaman4Ana-Maria eCebolla5Ana eBengoetxea6Yuri P Ivanenko7Bernard eDan8Guy eCheron9Guy eCheron10ULB Neuroscience Institute, Université Libre de BruxellesULB Neuroscience Institute, Université Libre de BruxellesFaculté Polytechnique de MonsFaculté Polytechnique de MonsULB Neuroscience Institute, Université Libre de BruxellesULB Neuroscience Institute, Université Libre de BruxellesULB Neuroscience Institute, Université Libre de BruxellesFondazione Santa LuciaULB Neuroscience Institute, Université Libre de BruxellesULB Neuroscience Institute, Université Libre de BruxellesUniversité de MonsThe existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00070/fullkinematicscentral pattern generator (CPG)human locomotionbiological oscillationsdynamical recurrent neural network (DRNN)neurophysiology of walking |
| spellingShingle | Thomas eHoellinger Mathieu ePetieau Matthieu eDuvinage Thierry eCastermans Karthik eSeetharaman Ana-Maria eCebolla Ana eBengoetxea Yuri P Ivanenko Bernard eDan Guy eCheron Guy eCheron Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator Frontiers in Computational Neuroscience kinematics central pattern generator (CPG) human locomotion biological oscillations dynamical recurrent neural network (DRNN) neurophysiology of walking |
| title | Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator |
| title_full | Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator |
| title_fullStr | Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator |
| title_full_unstemmed | Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator |
| title_short | Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator |
| title_sort | biological oscillations for learning walking coordination dynamic recurrent neural network functionally models physiological central pattern generator |
| topic | kinematics central pattern generator (CPG) human locomotion biological oscillations dynamical recurrent neural network (DRNN) neurophysiology of walking |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00070/full |
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