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Ideomotor feedback control in a recurrent neural network

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Abstract

The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better “biologically plausible” algorithm.

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References

  • Adams RA, Shipp S, Friston KJ (2013) Predictions not commands: active inference in the motor system. Brain Struct Funct 218(3):611–643

    Article  PubMed Central  PubMed  Google Scholar 

  • Åström KJ (2006) Introduction to stochastic control theory. Courier Dover Publications, New York

    Google Scholar 

  • Åström KJ, Hägglund T (2006) Advanced PID control. ISA-The Instrumentation, Systems, and Automation Society, Research Triangle Park

    Google Scholar 

  • Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  • Butler AB, Hodos W (2005) Comparative vertebrate neuroanatomy: evolution and adaptation. Wiley, New York

    Book  Google Scholar 

  • Chow TW, Fang Y (1998) A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics. IEEE Trans Ind Electron 45(1):151–161

    Article  Google Scholar 

  • Conant RC, Ashby W (1970) Every good regulator of a system must be a model of that system. Int J Syst Sci 1(2):89–97

    Article  Google Scholar 

  • Doya K (1993) Bifurcations of recurrent neural networks in gradient descent learning. IEEE Trans Neural Netw 1:75–80

    Google Scholar 

  • Ecker AS, Berens P, Keliris GA, Bethge M, Logothetis NK, Tolias AS (2010) Decorrelated neuronal firing in cortical microcircuits. Science 327(5965):584–587

    Article  CAS  PubMed  Google Scholar 

  • Farhang-Boroujeny B (1998) Adaptive filters: theory and applications. Wiley, New York

    Google Scholar 

  • Fortmann TE, Hitz KL (1977) An introduction to linear control systems. CRC Press, Boca Raton

    Google Scholar 

  • Friston KJ, Daunizeau J, Kilner J, Kiebel SJ (2010) Action and behavior: a free-energy formulation. Biol Cybern 102(3):227–260

    Article  PubMed  Google Scholar 

  • Gálvez-Carrillo M, De Keyser R, Ionescu C (2009) Nonlinear predictive control with dead-time compensator: application to a solar power plant. Solar Energy 83(5):743–752

    Article  Google Scholar 

  • Ge S, Hang CC, Lee TH, Zhang T (2010) Stable adaptive neural network control. Springer, New York

    Google Scholar 

  • Ge SS, Yang C, Lee TH (2008) Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time. IEEE Trans Neural Netw 19(9):1599–1614

    Article  PubMed  Google Scholar 

  • Gerstner W, Kistler WM (2002) Mathematical formulations of hebbian learning. Biol Cybern 87(5–6):404–415

    Article  PubMed  Google Scholar 

  • Greenwald AG (1970) Sensory feedback mechanisms in performance control: with special reference to the ideo-motor mechanism. Psychol Rev 77(2):73

    Article  CAS  PubMed  Google Scholar 

  • Gunnarsson S (1996) Combining tracking and regularization in recursive least squares identification. In: IEEE Conference on Decision and Control, vol 3, pp 2551–2552. Citeseer

  • Haykin SO (2014) Adaptive filter theory, 5th edn. Pearson Education. http://www.pearsonhighered.com/educator/product/Adaptive-Filter-Theory/9780132671453.page

  • Jaeger H (2001) The “echo state”approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148:34

  • Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667):78–80

    Article  CAS  PubMed  Google Scholar 

  • Jaeger H, Lukosevicius M, Popovici D, Siewert U (2007) Optimization and applications of Echo State Networks with leaky-integrator neurons. Neural Netw 20(3):335–352

    Article  PubMed  Google Scholar 

  • Jordan MI (1996) Computational aspects of motor control and motor learning. Handb Percept Action Motor Skills 2:71–118

    Google Scholar 

  • Jordan MI, Rumelhart DE (1992) Forward models: supervised learning with a distal teacher. Cogn Sci 16(3):307–354

    Article  Google Scholar 

  • Kawato M, Furukawa K, Suzuki R (1987) A hierarchical neural-network model for control and learning of voluntary movement. Biol Cybern 57(3):169–185

    Article  CAS  PubMed  Google Scholar 

  • Kwakernaak H, Sivan R (1972) Linear optimal control systems, vol 1. Wiley, New York

    Google Scholar 

  • Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat Neurosci 16(7):925–933

  • Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw 1(1):4–27

    Article  CAS  PubMed  Google Scholar 

  • Pan Y, Wang J (2012) Model predictive control of unknown nonlinear dynamical systems based on recurrent neural networks. IEEE Trans Ind Electron 59(8):3089–3101

    Article  Google Scholar 

  • Pearlmutter BA (1995) Gradient calculations for dynamic recurrent neural networks: a survey. IEEE Trans Neural Netw 6(5):1212–1228

    Article  CAS  PubMed  Google Scholar 

  • Prokhorov DV (2007) Training recurrent neurocontrollers for real-time applications. IEEE Trans Neural Netw 18(4):1003–1015

    Article  PubMed  Google Scholar 

  • Renart A, de la Rocha J, Bartho P, Hollender L, Parga N, Reyes A, Harris KD (2010) The asynchronous state in cortical circuits. Science 327(5965):587–590

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Seamans J, Durstewitz D (2008) Dopamine modulation. Scholarpedia 3(4):2711

    Article  Google Scholar 

  • Shin YK, Proctor RW, Capaldi E (2010) A review of contemporary ideomotor theory. Psychol Bull 136(6):943

    Article  PubMed  Google Scholar 

  • Skogestad S, Postlethwaite I (2007) Multivariable feedback control: analysis and design, vol 2. Wiley, New York

    Google Scholar 

  • Slotine J-JE, Li W et al (1991) Applied nonlinear control, vol 199. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Sontag ED (1997) Recurrent neural networks: Some systems-theoretic aspects. In: Karny M, Warwick K, Kurkova V (eds) Dealing with complexity: a neural network approach. Springer, London, pp 1–12

    Google Scholar 

  • Sussillo D, Abbott LF (2009) Generating coherent patterns of activity from chaotic neural networks. Neuron 63(4):544–557

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Tani J (1996) Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Trans Syst Man Cybern Part B Cybern 26(3):421–436

    Article  CAS  Google Scholar 

  • Waegeman T, Wyffels F, Schrauwen B (2012) Feedback control by online learning an inverse model. IEEE Trans Neural Netw Learn Syst 23(10):1637–1648

    Article  PubMed  Google Scholar 

  • Wang C, Hill DJ (2006) Learning from neural control. IEEE Trans Neural Netw 17(1):130–146

    Article  PubMed  Google Scholar 

  • Yang C, Ge SS, Xiang C, Chai T, Lee TH (2008) Output feedback nn control for two classes of discrete-time systems with unknown control directions in a unified approach. IEEE Trans Neural Netw 19(11):1873–1886

    Article  PubMed  Google Scholar 

  • Zhong-Sheng H (2006) On model-free adaptive control: the state of the art and perspective. Control Theory Appl 4:018

    Article  Google Scholar 

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Acknowledgments

I thank Herbert Jaeger, Michael Thon, Jochen Steil, Felix Reinhart, and Benjamin Schrauwen for helpful discussions. I was funded by the European project AMARSI.

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Authors and Affiliations

  1. Minds, Jacobs University Bremen, Bremen, Germany

    Mathieu Galtier

  2. NeuroMathComp, Inria Sophia, Sophia Antipolis, France

    Mathieu Galtier

  3. UNIC, CNRS, Gif-Sur-Yvette, France

    Mathieu Galtier

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  1. Mathieu Galtier
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Correspondence to Mathieu Galtier.

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Galtier, M. Ideomotor feedback control in a recurrent neural network. Biol Cybern 109, 363–375 (2015). https://doi.org/10.1007/s00422-015-0648-4

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  • DOI: https://doi.org/10.1007/s00422-015-0648-4

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