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Stochastic training of a biologically plausible spino-neuromuscular system model

Published: 07 July 2007 Publication History

Abstract

A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems that processes sensory information in a way similar to humans. Artificial neural networks have been an increasingly popular option for thisbecause they consist of processing units that approximate thesynaptic activity of biological signal processing units, i.e. neurons. In this paper we train a nonlinear recurrent spino-neuromuscular system model(SNMS) comparing the performance of genetic algorithms (GA)s, particle swarm optimizers (PSO)s, and GA/PSO hybrids. This model includes several key features of the SNMS that have previously been modeled individually but have not been combined into a single model as is done here. The results show that each algorithm produces fit solutions and generates fundamental biological behaviors that are not directly trained for such as tonic tension behaviors and tricepsactivation patterns.

References

[1]
M. Abeles, G. Hayton, and D. Lehmann. Modeling compositionality by dynamic binding of synfire chains. Journal of Computational Neuroscience, 17:179--201, 2004.
[2]
J. E. Baker and D. D. Thomas. A thermodynamic muscle model and a chemical basis for a.v. hill's muscle equation. Journal of Muscle Research and Cell Motility, 21: 335--344, 2000.
[3]
T. Bui, S. Cushing, D. Dewey, R. Eyffe, and P. Rose. Comparison of the morphological and electronic properties of renshaw cells, ia inhibitory interneurons, and motoneurons in the cat. Journal of Neurophysiology, 90:2900--2918, 2003.
[4]
B. Cartling. Control of computational dynamics of coupled integrate-and-fire neurons. Biological Cybernetics, 78: 383--395, 1997.
[5]
M. Clerc. Towards a deterministic and adaptive particle swarm optimization. In Proceedings of the Congress on Evolutionary Computation, pages 601--610, 1999.
[6]
N. Durand and J.-M. Alliot. Neural nets trained by genetic algorithms for collision avoidance. Applied Intelligence, 13:205--213, 2000.
[7]
A. Eiben and J. Smith. Introduction to Evolutionary Computing. Natural Computing. Springer, 1998.
[8]
G. J. Ettema and K. Meijer. Muscle contraction history: Modified hill versus an exponential decay model. Biological Cybernetics, 83:491--500, 2000.
[9]
D. Floreano and C. Mattiussi. Evolution of spiking neural controllers for autonomous vision-based robots. In Proceedings of the International Symposium on Evolutionary Robotics From Intelligent Robotics to Artificial Life, pages 38--61. Springer--Verlag, 2001.
[10]
M. Giuglaiano, M. Bove, and M. Grattarola. Activity driven computational strategies of a dynamically regulated integrate-and-fire model neuron. Journal of Computational Neuroscience, 7:247--254, 1999.
[11]
A. Hill. The heat of shortening and the dynamic constants of muscle. Proc. Roy. Soc. London B, 126(843):136--195, 1938.
[12]
E. R. Kandel, J. H. Schwartz, and T. M. Jessell. Priciples of Neural Science. McGraw-Hill, New York, 4 edition, 2000.
[13]
A. Kasinski and F. Ponulak. Experimantal Demonstration of Learning Properties of a New Supervised Learning Method for the Spiking Neural Networks, pages 145--152. Lecture Note in Computer Science. Springer Berlin / Heidelberg, 2005.
[14]
J. Kennedy and R. Eberhart. A discrete binary version of the particle swarm algorithm. In Proceedings of the Conference on Systems, Man, and Cybernetics, pages 4104--4109, 1997.
[15]
W. Maass and B. Ruf. The computational power of spiking neurons depends on the shape of the postsynaptic potentials. Electronic Colloquium on Computational Complexity (ECCC), 3(25), 1996.
[16]
M. Mandischer. Evolving recurrent neural networks with non-binary encoding. In IEEE International Conference on Evolutionary Computation, volume 2, pages 584--589, 1995.
[17]
T. A. McMachon. Muscles, Reflexes, and Locomotion. Princeton University Press, 1984.
[18]
N. Pavlidis, O. Tasoulis, V. Plagianakos, G. Nikiforidis, and M. Vrahatis. Spiking neural network training using evolutionary algorithms. In Proceedings of the 2005 International Joint Conference on Neural Networks, pages 2190--2194, 2005.
[19]
B. Ruf and M. Schmitt. Learning temporally encoded patterns in networks of spiking neurons. Neural Processing Letters, 5:9--18, 1997.
[20]
M. Settles and T. Soule. Breeding swarms: a ga/pso hybrid. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 161--168. ACM Press, 2005.
[21]
M. J. Shelley and L. Tao. Efficient and accurate time-stepping schemes for integrate-and-fire neuronal networks. Journal of Computational Neuroscience, 11:111--119, 2001.
[22]
G. Shepherd. Neurobiology. Oxford University Press, New York, 3 edition, 1994.
[23]
T. Wennekers and G. Palm. Controlling the speed of synfire chains. In International Conference on Artificial Neural Networks (ICANN), pages 451--456, Berlin, 1996. Springer.
[24]
X. Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423--1447, 1999.
[25]
A. Yazdanbakhsh, B. Babadi, S. Rouhani, E. Arabzadeh, and A. Abbassian. New attractor states for synchronous activity in synfire chains with excitatory and inhibitory coupling. Biological Cybernetics, 86:367--378, 2002.

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  • (2018)Stochastic optimization of a biologically plausible spino-neuromuscular system modelGenetic Programming and Evolvable Machines10.1007/s10710-007-9044-88:4(355-380)Online publication date: 24-Dec-2018

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  1. Stochastic training of a biologically plausible spino-neuromuscular system model

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 July 2007

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    Author Tags

    1. breeding swarm optimizers
    2. genetic algorithms
    3. neural networks
    4. particle swarm optimizers
    5. spiking networks
    6. spinal cord

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2018)Stochastic optimization of a biologically plausible spino-neuromuscular system modelGenetic Programming and Evolvable Machines10.1007/s10710-007-9044-88:4(355-380)Online publication date: 24-Dec-2018

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