Abstract
Experimental data have shown that inherent bursting of the neuron plays an important role in the generation of rhythmic movements in spinal networks. Based on the mechanism that the spinal neurons of a lamprey generate this inherent bursting, this paper builds a simplified inherent bursting neuron model. A new locomotion control neural network is built that takes advantage of this neuron model and its performance is analyzed mathematically and by numerical simulation. From these analyses, it is found that the new control networks have no restriction on their topological structure for generating the oscillatory outputs. If a network is used to control the motion of bionic robots or build the model of the vertebrate spinal circuitry, its topological structure can be constructed using the unit burst generator model proposed by Grillner. The networks can also be easily switched between oscillatory and non-oscillatory output. Additionally, inactivity and saturation properties of the new networks can also be developed, which will be helpful to increase the motor flexibility and environmental adaptability of bionic robots.
Similar content being viewed by others
References
Bicanski A, Ryczko D, Cabelguen J-M et al (2013) From lamprey to salamander: an exploratory modeling study on the architecture of the spinal locomotion networks in the salamander. Biol Cybern 107:565–587
Buchanan JT (1992) Neural network simulations of coupled locomotor oscillators in the lamprey spinal cord. Biol Cybern 66:367–374
Cohen AH, Holmes PJ, Rand RH (1982) The nature of the coupling between segmental oscillators of the lamprey spinal generator for locomotion: a mathematical model. J Math Biol 13:345–369
Collins J, Richmond S (1994) Hard-wired central pattern generators for quadrupedal locomotion. Biol Cybern 71:375–385
Ekeberg Ö (1993) A combined neuronal and mechanical model of fish swimming. Biol Cybern 69:363–374
Ermentrout GB (1992) Stable periodic solutions to discrete and continuum arrays of weakly coupled nonlinear oscillators. SIAM J Appl Math 52:1665–1687
Grillner S, Jessell TM (2009) Measured motion: searching for simplicity in spinal locomotor networks. Curr Opin Neurobiol 19:572–586
Guertin PA (2009) The mammalian central pattern generator for locomotion. Brain Res Rev 62:45–56
Hellgren J, Grillner S, Lansner A (1992) Computer simulation of the segmental neural network generating locomotion in lamprey by using populations of network interneurons. Biol Cybern 68:1–13
Huss M, Wang D, Trané C et al (2008) An experimentally constrained computational model of NMDA oscillations in lamprey CPG neurons. J Comput Neurosci 25:108–121
Ijspeert AJ (2008) Central pattern generators for locomotion control in animals and robots: a review. Neural Netw 21:642–653
Ijspeert AJ, Crespi A, Ryczko D et al (2007) From swimming to walking with a salamander robot driven by a spinal cord model. Science 315:1416–1420
Kopell N, Ermentrout GB (1988) Coupled oscillators and the design of central pattern generators. Math Biosci 90:87–109
Kopell N, Ermentrout GB (1991) On chains of oscillators forced at one end. SIAM J Appl Math 51:1397–1417
Kozlov A, Huss M, Lansner A et al (2009) Simple cellular and network control principles govern complex patterns of motor behavior. Proc Nati Acad Sci 106:20027–20032
Marder E, Calabrese RL (1996) Principles of rhythmic motor pattern generation. Physiol Rev 76:687–717
Massarelli N, Clapp G, Hoffman K et al (2016) Entrainment ranges for chains of forced neural and phase oscillators. J Math Neurosci 6:1–21
Matsuoka K (1985) Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biol Cybern 52:367–376
Matsuoka K (1987) Mechanisms of frequency and pattern control in the neural rhythm generators. Biol Cybern 56:345–353
McLean DL, Fan JY, Higashijima S-I et al (2007) A topographic map of recruitment in spinal cord. Nature 446:71–75
Previte JP, Sheils N, Hoffman KA et al (2011) Entrainment ranges of forced oscillators. J Math Biol 62:589–603
Righetti L, Buchli J, Ijspeert AJ (2006) Dynamic Hebbian learning in adaptive frequency oscillators. Phys D 216:269–281
Rybak IA, Shevtsova NA, Ptak K et al (2004) Intrinsic bursting activity in the pre-Bötzinger complex: role of persistent sodium and potassium currents. Biol Cybern 90:59–74
Tangorra JL, Mignano AP, Carryon GN et al. (2011) Biologically derived models of the sunfish for experimental investigations of multi-fin swimming. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 580–587
Wadden T, Hellgren J, Lansner A et al (1997) Intersegmental coordination in the lamprey: simulations using a network model without segmental boundaries. Biol Cybern 76:1–9
Wallén P, Williams TL (1984) Fictive locomotion in the lamprey spinal cord in vitro compared with swimming in the intact and spinal animal. J Physiol 347:225–239
Wallén P, Grillner S (1987) N-Methyl-D-aspartate receptor-induced, inherent oscillatory activity in neurons active during fictive locomotion in the lamprey. J Neurosci 7(9):2745–2755
Williams TL (1992) Phase coupling by synaptic spread in chains of coupled neuronal oscillators. Science 258:662–665
Williams TL, Sigvard KA, Kopell N et al (1990) Forcing of coupled nonlinear oscillators: studies of intersegmental coordination in the lamprey locomotor central pattern generator. J Neurophysiol 64:862–871
Yang GZ, Ma SG, Li B et al (2013) A hierarchical connectionist central pattern generator model for control three-dimensional gaits of snake-like robots. ACTA AUTOMATICA SINICA 39:1611–1622
Yu JZ, Tan M, Chen J et al (2014) A survey on cpg-inspired control models and system implementation. IEEE Trans Neural Netw 25:441–456
Yuste R, MacLean JN, Smith J et al (2005) The cortex as a central pattern generator. Nat Rev Neurosci 6:477–483
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61105110, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 14KJB510004 and the Lianyungang “521” Project and the six talent peaks project in Jiangsu Province, and the Jiangsu Overseas Research and Training Program for University Prominent Young and Middle-aged Teachers and President.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by J. Leo van Hemmen.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Appendix A
Appendix A
For the mechanical model of the lamprey, the parameters are shown in Table 3.
The muscles are simulated using a combination of a spring and a damper (Ekeberg 1993). Its model can be written as follows:
where \(M_f\) are the motoneuron activities of the flexor muscles; \(M_e\) are the motoneuron activities of the extensor muscles; \(\Delta \phi \) is the difference between the actual angle of the joint and its resting angle; and \(\alpha \), \(\beta \), \(\xi \), and \(\delta \) indicate the gain, the stiffness gain, the tonic stiffness, and the damping coefficient of the muscles, respectively. The parameters of all of the muscles are the same here: \(\alpha =1\) N m; \(\beta =0.3\) N m; \(\xi =10\), and \(\delta =5\) N m s.
The fluid forces \(F_i ,\,(i=1,\ldots ,41)\) of the links of the body can be calculated as
where \(\rho \) is the density of the water; \(l_i\) is the length of the link i of the body; \(h_i\) is the height of the link i of the body; \(C_i\) is the drag coefficient of the link i of the body (here \(C_i =1\) for all links of the body); \(v_i\) is the normal velocity of the midpoint of the link i.
Rights and permissions
About this article
Cite this article
Liu, Q., Wang, J.Z. Modeling and analysis of a new locomotion control neural networks. Biol Cybern 112, 345–356 (2018). https://doi.org/10.1007/s00422-018-0758-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00422-018-0758-x