Abstract
In general, the equivalent amplitude values and the specific phase differences between the oscillators/neurons are desired to obtain the smooth movements in the CPG based robotic applications. However, in the literature, the error minimization functions calculate either the amplitude or the phase errors between the nonlinear dynamics. This study offers an alternative error minimization approach. This approach calculates both the amplitude and the phase errors, simultaneously. The proposed approach, the RMS function and the phase error function have been utilized as the cost functions of the genetic and the ABC algorithms for the performance evaluation of the proposed approach. These functions have been assessed for estimating the coupling parameters of the electrically coupled HR neurons. According to the results, the proposed approach has minimum errors when compared with the other two functions. On the other hand, to utilize these estimated coupling parameters in the real-time applications, to create the CPG networks by using the coupled neurons and to use these emulated neurons in a locomotion control problem offer a particular importance for the developments in this field. Here, the HR neurons, which are coupled with the estimated parameters, have been implemented with FPGA device by using the SGDSP tool. Thus, the applicability to the real-time systems of the proposed approach has been verified with a hardware realization. Then, the trot gait pattern of a quadruped robot has been controlled by using these emulated neuronal responses, so the coupled biological neurons have been used as a controller in a CPG based multi-legged robotic application, successfully.













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Delcomyn F (1980) Neural basis for rhythmic behaviors in animals. Science 210:492–498
Selverston AI (2010) Invertebrate central pattern generator circuits. Philos Trans R Soc B 365:2329–2345
Ijspeert AJ (2008) Central pattern generators for locomotion control in animals and robots: a review. Neural Netw 21:642–653
Barron-Zambrano JH, Torres-Huitzil C (2013) FPGA implementation of a configurable neuromorphic CPG-based locomotion controller. Neural Netw 45:50–61
Yu J, Tan M, Chen J, Zhang J (2014) A survey on CPG-insipired control models and system implementation. IEEE Trans Neural Netw Learn Syst 25(3):441–456
Wang Q, Duan Z, Perc M, Chen G (2008) Synchronization transitions on small-world neuronal networks: effects of information transmission delay and rewiring probability. EPL (Europhys Lett) 83(5):50008
Wang Q, Perc M, Duan Z, Chen G (2009) Synchronization transitions on scale-free neuronal networks due to finite information transmission delays. Phys Rev E 80(2):026206
Wang Q, Chen G, Perc M (2011) Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling. PLoS ONE 6(1):e15851
Sun X, Lei J, Perc M, Kurths J, Chen G (2011) Burst synchronization transitions in a neuronal network of subnetworks. Chaos Interdiscip J Nonlinear Sci 21(1):016110
Dtchetgnia Djeundam SR, Yamapi R, Filatrella G, Kofane TC (2015) Stability of the synchronized network of Hindmarsh–Rose neuronal models with nearest and global couplings. Commun Nonlinear Sci Numer Simul 22:545–563
Nguyen LH, Hong KS (2013) Adaptive synchronization of two coupled chaotic Hindmarsh–Rose neurons by controlling the membrane potential of a slave neuron. Appl Math Model 37:2460–2468
Deng B, Wang J, Fei X (2006) Synchronizing two coupled chaotic neurons in external electrical stimulation using backstepping control. Chaos Solitons Fract 29:182–189
Wang J, Chen LS, Deng B (2009) Synchronization of Ghostburster neuron in external electrical stimulation via H-infinity variable universe fuzzy adaptive control. Chaos Solitons Fract 39:2076–2085
Chen M (2007) Synchronization in time-varying networks: a matrix measure approach. Phys Rev E 76:016104
Li Z (2008) Exponential stability of synchronization in asymmetrically coupled dynamical networks. Chaos Interdiscip J Nonlinear Sci 18(2):023124
Li Z, Lee J (2007) New eigenvalue based approach to synchronization in asymmetrically coupled networks. Chaos Interdiscip J Nonlinear Sci 17(4):043117
Ge ZM, Chen C-C (2004) Phase synchronization of coupled chaotic multiple time scales systems. Chaos Solitons Fract 20(3):639–647
Pikovsky Arkady S, Michael Rosenblum G, Grigory Osipov V, Kurths J (1997) Phase synchronization of chaotic oscillators by external driving. Phys D Nonlinear Phenom 104(3–4):219–238
Ma J, Mi L, Zhou P, Xu Y, Hayat T (2017) Phase synchronization between two neurons induced by coupling of electromagnetic field. Appl Math Comput 307:321–328
Shuai JW, Durand DM (1999) Phase synchronization in two coupled chaotic neurons. Phys Lett A 264(4):289–297
Jalili M (2011) Phase synchronizing in Hindmarsh–Rose neural networks with delayed chemical coupling. Neurocomputing 74(10):1551–1556
Chen Q, Wang J, Yang S, Qin Y, Deng B, Wei X (2017) A real-time FPGA implementation of a biologically inspired central pattern generator network. Neurocomputing 244:63–80
Soleimani H, Ahmadi A, Bavandpour M (2012) Biologically inspired spiking neurons: piecewise linear models and digital implementation. IEEE Trans Circuits Syst I Reg Pap 59:2991–3004
Geit WV, Schutter ED, Achard P (2008) Automated neuron model optimization techniques: a review. Biol Cybern 99:241–251
Lu W, Chen T (2006) New approach to synchronization analysis of linearly coupled ordinary differential systems. Phys D Nonlinear Phenom 213:214–230
Chen W, Ren G, Zhang J, Wang J (2012) Smooth transition between different gaits of a hexapod robot via a central pattern generators algorithm. J Intell Robot Syst 67:255–270
Inagaki S, Yuasa H, Suzuki T, Arai T (2006) Wave CPG model for autonomous decentralized multi-legged robot: gait generation and walking speed control. Robot Auton Syst 54:118–126
Ijspeert AJ, Crespi A, Ryczko D, Cabelguen JM (2007) From swimming to walking with a salamander robot driven by a spinal cord model. Science 315(5817):1416–1420
Carla Pinto MA, Tenreiro Machado JA (2010) Fractional central pattern generators for bipedal locomotion. Nonlinear Dyn 62:27–37
Ortega-Zamorano F, Jerez JM, Juárez GE, Franco L (2017) FPGA implementation of neurocomputational models: comparison between standard back-propagation and C-Mantec constructive algorithm. Neural Process Lett 46(3):899–914
Arena P, Fortuna L, Frasca M, Sicurella G (2004) An adaptive, self-organizing dynamical system for hierarchical control of bio-inspired locomotion. IEEE Trans Syst Man Cybern B 34(4):1823–1837
Guerra-Hernandez EI, Espinal A, Batres-Mendoza P, Garcia-Capulin CH, Romero-Troncoso RDJ, Rostro-Gonzalez H (2017) A FPGA-based neuromorphic locomotion system for multi-legged robots. IEEE Access 5:8301–8312
Espinal A, Rostro-Gonzalez H, Carpio M, Guerra-Hernandez EI, Ornelas-Rodriguez M, Sotelo-Figueroa M (2016) Design of spiking central pattern generators for multiple locomotion gaits in hexapod robots by christiansen grammar evolution. Front Neurorobot 10:6
Filho AC, Dutra MS, Raptopoulos LS (2005) Modeling of a bipedal robot using mutually coupled Rayleigh oscillators. Biol Cybern 92(1):1–7
Zhang D, Zhang Q, Zhu X (2015) Exploring a type of central pattern generator based on Hindmarsh–Rose model: from theory to application. Int J Neural Syst 25(01):1450028
Rostro-Gonzalez H, Cerna-Garcia PA, Trejo-Caballero G, Garcia-Capulin CH, Ibarra-Manzano MA, Avina-Cervantes JG, Torres-Huitzil C (2015) A CPG system based on spiking neurons for hexapod robot locomotion. Neurocomputing 170:47–54
Lee YJ, Lee J, Kim K, Kim YB, Ayers J (2007) Low power CMOS electronic central pattern generator design for a biomimetic underwater robot. Neurocomputing 71(1):284–296
Ambroise M, Levi T, Joucla S, Yvert B, Saighi S (2013) Real-time biomimetic central pattern generators in an FPGA for hybrid experiments. Front Neurosci 7:215
Heidarpur M, Ahmadi A, Kandalaft N (2017) A digital implementation of 2D Hindmarsh-Rose neuron. Nonlinear Dyn 89:2259–2272
Zhang J, Huang S, Pang S, Wang M, Gao S (2016) Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network. Nonlinear Dyn 84:1303–1310
Barron-Zambrano JH, Torres-Huitzil C (2011) Two-phase GA parameter tunning method of CPGs for quadruped gaits. In: International joint conference on neural networks, San Jose, California, USA, pp 1767–1774
Dahasert N, Öztürk İ, Kiliç R (2012) Experimental realizations of the HR neuron model with programmable hardware and synchronization applications. Nonlinear Dyn 70(4):2343–2358
Korkmaz N, Öztürk İ, Kılıç R (2016) The investigation of chemical coupling in a HR neuron model with reconfigurable implementations. Nonlinear Dyn 86(3):1841–1854
Elson RC, Selverston AI, Huerta R, Rulkov NF, Rabinovich AI, Abarbanel HDI (1998) Synchronous behavior of two coupled biological neurons. Phys Rev Lett 81(25):5692–5695
Hindmarsh JL, Rose RM (1984) A model of neural bursting using three couple first order differential equations. Proc R Soc Lond Biol Sci 221(1222):87–102
Zhang JQ, Huang SF, Pang ST, Wang MS, Gao S (2015) Synchronization in the uncoupled neuron system. Chin Phys Lett 32(12):9–13
Wu K, Wang T, Wang C, Du T, Lu H (2016) Study on electrical synapse coupling synchronization of Hindmarsh–Rose neurons under Gaussian white noise. Neural Comput Appl 30(2):551–561
Chen Y, Li L, Peng H, Xiao J, Yang Y, Shi Y (2017) Particle swarm optimizer with two differential mutation. Appl Soft Comput 61:314–330
Awad NH, Ali MZ, Suganthan PN, Reynolds RG (2017) CADE: a hybridization of cultural algorithm and differential evolution for numerical optimization. Inf Sci 378:215–241
Sharma TK, Pant M (2017) Shuffled artificial bee colony algorithm. Soft Comput 21(20):6085–6104
Hu R, Wen S, Zeng Z, Huang T (2017) A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221:24–31
Li L, Yang Y, Peng H, Wang X (2006) Parameters identification of chaotic systems via chaotic ant swarm. Chaos Solitons Fract 28(5):1204–1211
Haupt RL, Haupt SE (2004) Practical genetic algorithms, 2nd edn. Wiley, New York
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department
Dang TL, Hoshino Y (2018) Hardware/software co-design for a neural network trained by particle swarm optimization algorithm. Neural Process Lett 49:1–25
Huang HC, Chiang CH (2016) An evolutionary radial basis function neural network with robust genetic-based immunecomputing for online tracking control of autonomous robots. Neural Process Lett 44(1):19–35
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This research is supported by FDK-2016-6719 code project of Scientific Research Projects Coordination Unit of Erciyes University.
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Korkmaz, N., Kılıç, R. An Alternative Approach for Setting the Optimum Coupling Parameters Among the Neural Central Pattern Generators Considering the Amplitude and the Phase Error Calculations. Neural Process Lett 50, 645–667 (2019). https://doi.org/10.1007/s11063-019-10070-4
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DOI: https://doi.org/10.1007/s11063-019-10070-4