Abstract
In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.
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References
Angarita-Jaimes, N., Dewhirst, O. P., Simpson, D. M., Kondoh, Y., Allen, R., & Newland, P. L. (2012). The dynamics of analogue signalling in local networks controlling limb movement. European Journal of Neuroscience, 36(9), 3269–3282.
Angeline, P. J., Saunders, G. M., & Pollack, J. B. (1994). An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5(1), 54–65.
Au, S. K., & Herr, H. M. (2008). Powered ankle-foot prosthesis. IEEE Robotics and Automation Magazine, 15(3), 52–59.
Bares, J. E. (1999). Dante II: technical description, results, and lessons learned. The International Journal of Robotics Research, 18(7), 621–649.
Beer, R. D., Quinn, R. D., Chiel, H. J., & Ritzmann, R. E. (1997). Biologically inspired approaches to robotics: what can we learn from insects? Communications of the ACM, 40(3), 30–38.
Bishop, C. M., Lange, N., & Ripley, B. D. (1995). Neural networks for pattern recognition (Vol. 92). London: Oxford University Press.
Burrows, M. (1996). The neurobiology of an insect brain. Oxford: Oxford University Press.
Burrows, M., & Horridge, G. A. (1974). The organization of inputs to motoneurons of the locust metathoracic leg. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences, 269(896), 49–94.
Büschges, A., & Gruhn, M. (2007). Mechanosensory feedback in walking: from joint control to locomotor patterns. In Insect mechanics and control (Vol. 34, pp. 193–230). Academic Press.
Büschges, A., Kittmann, R., & Schmitz, J. (1994). Identified nonspiking interneurons in leg reflexes and during walking in the stick insect. Journal of Comparative Physiology A, 174(6), 685–700.
Chen, D., Yin, J., Zhao, K., Zheng, W., & Wang, T. (2011). Bionic mechanism and kinematics analysis of hopping robot inspired by locust jumping. Journal of Bionic Engineering, 8(4), 429–439.
Clarac, F., Vedel, J. P., & Bush, B. M. (1978). Intersegmental reflex coordination by a single joint receptor organ (CB) in rock lobster walking legs. The Journal of Experimental Biology, 73, 29–46.
Costalago Meruelo, A., Simpson, D. M., Veres, S. M., & Newland, P. L. (2016). Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron. Neural Networks, 75, 56–65.
Cruse, H., Dautenhahn, K., & Schreiner, H. (1992). Coactivation of leg reflexes in the stick insect. Biological Cybernetics, 67(4), 369–375.
Cruse, H., Kindermann, T., Schumm, M., Dean, J., & Schmitz, J. (1998). Walknet—a biologically inspired network to control six-legged walking. Neural Networks, 11(7–8), 1435–1447.
Delcomyn, F. (2004). Insect walking and robotics. Annual Review of Entomology, 49, 51–70.
Delcomyn, F., & Nelson, M. E. (2000). Architectures for a biomimetic hexapod robot. Robotics and Autonomous Systems, 30(1), 5–15.
Dewhirst, O. P. (2012). Nonlinear system analysis of local reflex control of locust hind limbs by, PhD thesis, University of Southampton.
Dewhirst, O. P., Angarita-Jaimes, N., Simpson, D. M., Allen, R., & Newland, P. L. (2013). A system identification analysis of neural adaptation dynamics and nonlinear responses in the local reflex control of locust hind limbs. Journal of Computational Neuroscience, 34(1), 39–58.
Dürr, V., Schmitz, J., & Cruse, H. (2004). Behaviour-based modelling of hexapod locomotion: linking biology and technical application. Arthropod Structure and Development, 33(3), 237–250.
Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing. Springer Science & Business Media.
Endo, W., Santos, F. P., Simpson, D., Maciel, C. D., & Newland, P. L. (2015). Delayed mutual information infers patterns of synaptic connectivity in a proprioceptive neural network. Journal of Computational Neuroscience, 38(2), 427–438.
Espenschied, K. S., Chiel, H. J., Quinn, R. D., & Beer, R. D. (1993). Leg coordination mechanisms in the stick insect applied to hexapod robot locomotion. Adaptive Behavior, 1(4), 455–468.
Espenschied, K. S., Quinn, R. D., Beer, R. D., & Chiel, H. J. (1996). Biologically based distributed control and local reflexes improve rough terrain locomotion in a hexapod robot. Robotics and Autonomous Systems, 18(1–2), 59–64.
Faisal, A., Selen, L. P. J., & Wolpert, D. M. (2008). Noise in the nervous system. Nature Reviews. Neuroscience, 9(4), 292–303.
Field, L. H., & Burrows, M. (1982). Reflex effects of the femoral chordotonal organ upon leg motor neurones of the locust. Journal of Experimental Biology, 101(1), 265–285.
Field, L. H., & Rind, F. C. (1981). A single insect chordotonal organ mediates inter-and intra-segmental leg reflexes. Comparative Biochemistry and Physiology Part A, 68(1), 99–102.
Gandevia, S. C., Refshauge, K. M., & Collins, D. F. (2002). Proprioception: peripheral inputs and perceptual interactions BT - sensorimotor control of movement and posture. Boston: Springer.
Goble, D. J., Coxon, J. P., Wenderoth, N., Van Impe, A., & Swinnen, S. P. (2009). Proprioceptive sensibility in the elderly: degeneration, functional consequences and plastic-adaptive processes. Neuroscience and Biobehavioral Reviews, 33(3), 271–278.
Halbertsma, J. M. (1983). The stride cycle of the cat: the modelling of locomotion by computerized analysis of automatic recordings. Acta Physiologica Scandinavica. Supplementum, 521, 1–75.
Hanson, M. A., Burton, A. K., Kendall, N. A. S., Lancaster, R. J., & Pilkington, A. (2006). The costs and benefits of active case management and rehabilitation for musculoskeletal disorders, Prepared by Hu-Tech Associates Ltd for the Health and Safety Executive, London, 2006.
Haykin, S. (2004). Neural networks: a comprehensive foundation (Vol. 2). Englewood Cliffs: Prentice Hall.
He, J., Maltenfort, M., Wang, Q. W. Q., & Hamm, T. (2001). Learning from biological systems: modeling neural control. IEEE Control Systems Magazine, 21(4), 55–69.
Ijspeert, A. J. (2008). Central pattern generators for locomotion control in animals and robots: a review. Neural Networks, 21(4), 642–653.
Jiménez-Fabián, R., & Verlinden, O. (2012). Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Medical Engineering and Physics, 34(4), 397–408.
John, H. (1992). Holland, Adaptation in natural and artificial systems. Cambridge: MIT Press.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95—international conference on neural networks. (Vol. 4, pp. 1942–1948). IEEE.
Kondoh, Y., Okuma, J., & Newland, P. L. (1995). Dynamics of neurons controlling movements of a locust hind leg: Wiener kernel analysis of the responses of proprioceptive afferents. Journal of Neurophysiology, 73(5), 1829–1842.
Kovač, M., Fuchs, M., Guignard, A., Zufferey, J. C., & Floreano, D. (2008). A miniature 7g jumping robot. In Proceedings—IEEE international conference on robotics and automation (pp. 373–378).
Lewinger, W. A., Reekie, H. M., & Webb, B. (2011). A hexapod robot modeled on the stick insect. In IEEE 15th international conference on advanced robotics: new boundaries for robotics (pp. 541–548). ICAR 2011’.
Ljung, L. (1998). System identification. In Signal analysis and prediction (pp. 163–173). Springer.
Marder, E., & Taylor, A. L. (2011). Multiple models to capture the variability in biological neurons and networks. Nature Neuroscience, 14(2), 133–138.
Marmarelis, V. Z. (2004). Nonlinear dynamic modeling of physiological systems (Vol. 10). New York: Wiley.
Newland, P. L., & Kondoh, Y. (1997a). Dynamics of neurons controlling movements of a locust hind leg II. Flexor tibiae motor neurons. Journal of Neurophysiology, 77(4), 1731–1746.
Newland, P. L., & Kondoh, Y. (1997b). Dynamics of neurons controlling movements of a locust hind leg. III. Extensor tibiae motor neurons. Journal of Neurophysiology, 77(6), 3297–3310.
Pearson, K. G. (1993). Common principles of motor control in vertebrates and invertebrates. Annual Review of Neuroscience, 16, 265–297.
Pearson, K. G. (1995). Proprioceptive regulation of locomotion. Current Opinion in Neurobiology, 5(6), 786–791.
Ritzmann, R. E., & Büschges, A. (2007). Adaptive motor behavior in insects. Current Opinion in Neurobiology, 17(6), 629–636.
Ritzmann, R. E., Quinn, R. D., & Fischer, M. S. (2004). Convergent evolution and locomotion through complex terrain by insects, vertebrates and robots. Arthropod Structure and Development, 33(3), 361–379.
Rushton, D. N. (1997). Functional electrical stimulation. Physiological Measurements, 18(4), 241–75.
Schneidman, E., Brenner, N., Tishby, N., van Steveninck, R. R. D. R., & Bialek, W. (2000). Universality and individuality in a neural code. ArXiv Physics e-prints p. 16.
Shultz, A. H., Lawson, B. E., & Goldfarb, M. (2016). Variable cadence walking and ground adaptive standing with a powered ankle prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24 (4), 495–505.
Sietsma, J., & Dow, R. J. F. (1991). Creating artificial neural networks that generalize. Neural Networks, 4 (1), 67–79.
Stewart, J. D. (2008). Foot drop: where, why and what to do? Practical Neurology, 8(3), 158–169.
Suraweera, N. P., & Ranasinghe, D. N. (2008). A natural algorithmic approach to the structural optimisation of neural networks. In Proceedings of the 2008 4th international conference on information and automation for sustainability (pp. 150–156). ICIAFS 2008.
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989). Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech and Signal Processing, 37(3), 328–339.
Webb, B. (2002). Robots in invertebrate neuroscience. Nature, 417(6886), 359–363.
Webb, B, Harrison, R. R., & Willis, M. A. (2004). Sensorimotor control of navigation in arthropod and arti cial systems.
Yao, X. (1999). Evolving artificial neural networks. In Proceedings of the IEEE (Vol. 87, pp. 1423–1447).
Acknowledgements
Alicia Costalago-Meruelo was supported by an EPRSC grant (EP/G03690X/1) from The Institute of Sound and Vibration Research and the Institute for Complex Systems Simulations at the University of Southampton. The data is freely available through the Southampton University repository under. doi:10.5258/SOTON/D0014.
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Costalago-Meruelo, A., Simpson, D.M., Veres, S.M. et al. Predictive control of intersegmental tarsal movements in an insect. J Comput Neurosci 43, 5–15 (2017). https://doi.org/10.1007/s10827-017-0644-x
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DOI: https://doi.org/10.1007/s10827-017-0644-x