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Anticipation in Neurocybernetics

  • Slawomir J. NasutoEmail author
  • Yoshikatsu Hayashi
Living reference work entry

Abstract

There is an increasing recognition in cognitive sciences of the need to account for the fundamental role of anticipation; an ability exhibited by living systems to orient toward future. Anticipation has been even proposed in psychology as the foundational building block of cognition. More recently, cognitive sciences and philosophy of mind have seen proliferation of theories varying in flavor and emphasis but common in postulating the fundamental role of predictive inference in the brain’s function. These accounts are steeped in computational cognitive paradigm and a contributing factor to their rapid gains in popularity is their consistency with the mechanistic view dominant in sciences. However, due to this very computational commitment, these accounts face serious philosophical problems, in common with other computational approaches, in their inability to provide a satisfactory explanation of the most fundamental properties of our mental life. Robert Rosen offered an alternative account of living systems, which inextricably links life and cognition. His postulate is to extend narrowly constructed mechanistic (and henceforth computational) account beyond classical Newtonian mechanics to a framework recognizing that living systems constitute a distinct class of complex systems and positioning anticipation as the fundamental characteristics distinguishing them from inanimate ones. Anticipating synchronization, a recently discovered intriguing behavior of some dynamical systems, offers a possibility of explaining anticipation from ground up, based on simple features abundant in living systems in general, and in the nervous system, in particular. A review of the studies investigating the anticipating synchronization and its extensions is provided, and a special emphasis is placed on the investigations concerned with its neurobiological plausibility. The core definition of anticipation according to Rosen postulates the ability of an organism to change its behavior in view of potential future outcomes. Thus, an overview is included of research on motion coordination that is grounded in dynamics and also concerned with the role of anticipation in movement.

Keywords

Anticipation Anticipatory systems Anticipating synchronization Neurocybernetics 

References

  1. Aitchison, L., & Lengyel, M. (2017). With or without you: Predictive coding and Bayesian inference in the brain. Current Opinion in Neurobiology, 46, 219–227.  https://doi.org/10.1016/j.conb.2017.08.010. (Computational Neuroscience).CrossRefGoogle Scholar
  2. Akrami, A., Kopec, C. D., Diamond, M. E., & Brody, C. D. (2018). Posterior parietal cortex represents sensory history and mediates its effects on behaviour. Nature, 554, 368–372.  https://doi.org/10.1038/nature25510.CrossRefGoogle Scholar
  3. Bickhard, M. H. (2009). The biological foundations of cognitive science. New Ideas in Psychology, 27(1), 75–84.  https://doi.org/10.1016/j.newideapsych.2008.04.001.CrossRefGoogle Scholar
  4. Brovelli, A., Ding, M., Ledberg, A., Chen, Y., Nakamura, R., & Bressler, S. L. (2004). Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by granger causality. Proceedings of the National Academy of Sciences, 101(26), 9849–9854.  https://doi.org/10.1073/pnas.0308538101.CrossRefGoogle Scholar
  5. Buck, C. L. (2018). Hibernation: Life in the fast lane. eLife, 7, e35029.CrossRefGoogle Scholar
  6. Butz, M., Sigaud, O., Pezzulo, G., & Baldassarre, G. (2007). Anticipatory behavior in adaptive learning systems: From brains to individual and social behavior. Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  7. Calvo, O., Chialvo, D., Eguíluz, V., Mirasso, C., & Toral, R. (2004). Anticipated synchronization: A metaphorical linear view. Chaos, 14, 7–13.  https://doi.org/10.1063/1.1620991.CrossRefGoogle Scholar
  8. Ciszak, M., Calvo, O., Masoller, C., Mirasso, C. R., & Toral, R. (2003). Anticipating the response of excitable systems driven by random forcing. Physical Review Letters, 90, 204102.  https://doi.org/10.1103/PhysRevLett.90.204102.CrossRefGoogle Scholar
  9. Ciszak, M., Marino, F., Toral, R., & Balle, S. (2004a). Dynamical mechanism of anticipating synchronization in excitable systems. Physical Review Letters, 93, 114102.  https://doi.org/10.1103/PhysRevLett.93.114102.CrossRefGoogle Scholar
  10. Ciszak, M., Toral, R., & Mirasso, C. (2004b). Coupling and feedback effects in excitable systems: Anticipated synchronization. Modern Physics Letters B, 18(23), 1135–1155.  https://doi.org/10.1142/S0217984904007694.CrossRefGoogle Scholar
  11. Ciszak, M., Mirasso, C. R., Toral, R., & Calvo, O. (2009). Predict-prevent control method for perturbed excitable systems. Physical Review E, 79, 046203.  https://doi.org/10.1103/PhysRevE.79.046203.CrossRefGoogle Scholar
  12. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204.  https://doi.org/10.1017/S0140525X12000477.CrossRefGoogle Scholar
  13. Clark, A. (2015). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press. Retrieved from https://books.google.co.uk/books?id=TnqECgAAQBAJ
  14. Corron, N. J., Blakely, J. N., & Pethel, S. D. (2005). Lag and anticipating synchronization without time-delay coupling. Chaos: An Interdisciplinary Journal of Nonlinear Science, 15(2), 023110.  https://doi.org/10.1063/1.1898597.CrossRefGoogle Scholar
  15. Dennett, D. (1996). Darwin’s dangerous idea: Evolution and the meanings of life. Simon & Schuster. Retrieved from https://books.google.co.uk/books?id=FvRqtnpVotwC
  16. Eberle, H., Nasuto, S., & Hayashi, Y. (2018). Anticipation from sensation: Using anticipating synchronisation to stabilise a system with inherent sensory delay. Royal Society Open Science, 5, 171314.CrossRefGoogle Scholar
  17. Ekman, M., Kok, P., & de Lange, F. P. (2017). Time-compressed preplay of anticipated events in human primary visual cortex. Nature Communications, 8, 15276.  https://doi.org/10.1038/ncomms15276.CrossRefGoogle Scholar
  18. Engstrom, D. (1996). Reaction-anticipation transitions in human perception-action patterns. Human Movement Science, 15, 809–832.CrossRefGoogle Scholar
  19. Haggard, P. (2017). Sense of agency in the human brain [Journal Article]. Nature Reviews Neuroscience, 18, 196.  https://doi.org/10.1038/nrn.2017.14.CrossRefGoogle Scholar
  20. Hayashi, Y., & Kondo, T. (2013). Mechanism for synchronized motion between two humans in mutual tapping experiments: Transition from alternative mode to synchronization mode. Physical Review E, 88(2), 022715.CrossRefGoogle Scholar
  21. Hayashi, Y., & Sawada, Y. (2013). Transition from an anti-phase error-correction-mode to a synchronization mode in the mutual hand tracking. Physical Review E, 88(2), 022704.CrossRefGoogle Scholar
  22. Hayashi, Y., Tamura, Y., Sase, K., Sugawara, K., & Sawada, Y. (2011). Intermittently-visual tracking experiments reveal the roles of error-correction and predictive mechanisms in the human visual-motor control system. Transactions of the Society of Instrument and Control Engineers, 46(7), 391–400.CrossRefGoogle Scholar
  23. Hayashi, Y., Blake, J., & Nasuto, S. (2015). Anticipatory engineering: Anticipation in sensory-motor systems of human. In Anticipation across disciplines (Cognitive systems monographs). Switzerland: Springer International Publishing.Google Scholar
  24. Hayashi, Y., Nasuto, S. J., & Eberle, H. (2016). Renormalized time scale for anticipating and lagging synchronization. Physical Review E, 93(5), 052229.CrossRefGoogle Scholar
  25. Hesslow, G. (2012). The current status of the simulation theory of cognition. Brain Research, 1428, 71–79.  https://doi.org/10.1016/j.brainres.2011.06.026. Retrieved from http://www.sciencedirect.com/science/article/pii/S0006899311011309. (The Cognitive Neuroscience of Thought).
  26. Ikegami, T., Hirashima, M., Taga, G., & Nozaki, D. (2010). Asymmetric transfer of visuomotor learning between discrete and rhythmic movements. The Journal of Neuroscience, 30(12), 4515–4521.CrossRefGoogle Scholar
  27. Ishida, F., & Sawada, Y. (2013). Human hand moves proactively to the external stimulus; An evolutional strategy for minimizing transient error. Physical Review E, 93, 168105.Google Scholar
  28. Jordan, D., Smith, P., & Smith, P. (2007). Nonlinear ordinary differential equations: An introduction for scientists and engineers. Oxford: Oxford University Press. Retrieved from https://books.google.co.uk/books?id=KpASDAAAQBAJ
  29. Kawato, M. (1999). Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9, 718.CrossRefGoogle Scholar
  30. Khalil, H. (2002). Nonlinear systems (3rd ed.). New Jersey: Prentice Hall.Google Scholar
  31. Liang, H., & Wang, H. (2003). Top-down anticipatory control in prefrontal cortex. Theory in Biosciences, 122(1), 70–86.  https://doi.org/10.1007/s12064-003-0038-7.CrossRefGoogle Scholar
  32. Louie, A. H. (2009). More than life itself: A synthetic continuation in relational biology. ontos verlag, Frankfurt [now De Gruyter, Berlin].Google Scholar
  33. Louie, A. H. (2010). Robert Rosen’s anticipatory systems. Foresight, 12(3), 18–29.CrossRefGoogle Scholar
  34. Louie, A. H. (2019). Relational biology. In R. Poli (Ed.), Handbook of anticipation: Theoretical and applied aspects of the use of future in decision making (pp. 1–28). Cham: Springer International Publishing.Google Scholar
  35. Louie, A. H., & Poli, R. (2019). Complex systems. In R. Poli (Ed.), Handbook of anticipation: Theoretical and applied aspects of the use of future in decision making (pp. 1–19). Cham: Springer International Publishing.Google Scholar
  36. Marzen, S. E., & Crutchfield, J. P. (2018). Optimized bacteria are environmental prediction engines. Phys. Rev. E 98, 012408.Google Scholar
  37. Mates, J. (1994). A model of synchronization of motor acts to a stimulus sequence. I. Timing and error corrections. Biological Cybernetics, 70(5), 463–473.CrossRefGoogle Scholar
  38. Matias, F. S., Carelli, P. V., Mirasso, C. R., & Copelli, M. (2011). Anticipated synchronization in a biologically plausible model of neuronal motifs. Physical Review E, 84, 021922.  https://doi.org/10.1103/PhysRevE.84.021922.CrossRefGoogle Scholar
  39. Matias, F. S., Gollo, L. L., Carelli, P. V., Bressler, S. L., Copelli, M., & Mirasso, C. R. (2014). Modeling positive granger causality and negative phase lag between cortical areas. NeuroImage, 99, 411–418.  https://doi.org/10.1016/j.neuroimage.2014.05.063.CrossRefGoogle Scholar
  40. Matias, F. S., Carelli, P. V., Mirasso, C. R., & Copelli, M. (2015). Self-organized near-zero-lag synchronization induced by spike-timing dependent plasticity in cortical populations. PLoS One, 10(10), 1–18.  https://doi.org/10.1371/journal.pone.0140504.CrossRefGoogle Scholar
  41. Matias, F. S., Gollo, L. L., Carelli, P. V., Mirasso, C. R., & Copelli, M. (2016). Inhibitory loop robustly induces anticipated synchronization in neuronal microcircuits. Physical Review E, 94, 042411.  https://doi.org/10.1103/PhysRevE.94.042411.CrossRefGoogle Scholar
  42. Maturana, H., & Varela, F. (1991). Autopoiesis and cognition: The realization of the living. Springer Netherlands.Google Scholar
  43. Middleton, A. (2000). Basal ganglia and cereberllar loops: Motor and cognitive circuits. Brain Research Reviews, 31, 236–250.CrossRefGoogle Scholar
  44. Mirasso, C. R., Carelli, P. V., Pereira, T., Matias, F. S., & Copelli, M. (2017). Anticipated and zero-lag synchronization in motifs of delay-coupled systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(11), 114305.  https://doi.org/10.1063/1.5006932.CrossRefGoogle Scholar
  45. Mita, A., Mushiake, H., Shima, K., Matsuzaka, Y., & Tanji, J. (2009). Interval time coding by neurons in the presupplementary and supplementary motor areas. Nature Neuroscience, 12, 502–507.CrossRefGoogle Scholar
  46. Nasuto, S. J., & Hayashi, Y. (2016a). Anticipation: Beyond synthetic biology and cognitive robotics. BioSystems, 148, 22–31.CrossRefGoogle Scholar
  47. Nasuto, S. J., & Hayashi, Y. (2016b). Synapses in digital medium: Computational investigations of neural basis of anticipation. In M. Nadin (Ed.), Anticipation across disciplines (pp. 187–201). Cham: Springer International Publishing.CrossRefGoogle Scholar
  48. Oguchi, T. (2007). A finite spectrum assignment for retarded non-linear systems and its solvability condition. International Journal of Control, 80(6), 898–907.  https://doi.org/10.1080/17499510701204166.CrossRefGoogle Scholar
  49. Oguchi, T. (2017). Anticipating synchronization and state predictor for nonlinear systems. In N. van de Wouw, E. Lefeber, & I. Lopez Arteaga (Eds.), Nonlinear systems: Techniques for dynamical analysis and control (pp. 103–122). Cham: Springer International Publishing.CrossRefGoogle Scholar
  50. Oguchi, T., & Nijmeijer, H. (2005a). Control of nonlinear systems with time-delay using state predictor based on synchronization. In Proceedings of the ENOC 2005 (pp. 1150–1156).Google Scholar
  51. Oguchi, T., & Nijmeijer, H. (2005b). Prediction of chaotic behavior. IEEE Transactions on Circuits and Systems, 52-I(11), 2464–2472.  https://doi.org/10.1109/TCSI.2005.853396.CrossRefGoogle Scholar
  52. Oguchi, T., & Nijmeijer, H. (2006). Anticipating synchronization of nonlinear systems with uncertainties. IFAC Proceedings Volumes, 39(10), 290–295.  https://doi.org/10.3182/20060710-3-IT-4901.00048. (6th IFAC Workshop on Time Delay Systems).CrossRefGoogle Scholar
  53. Pezzulo, G., Butz, M., Sigaud, O., & Baldassarre, G. (2009). Anticipatory behavior in adaptive learning systems: From psychological theories to artificial cognitive systems. Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  54. Port, R., & Van Gelder, T. (1998). Mind as motion: Explorations in the dynamics of cognition. MIT Press. Retrieved from https://books.google.co.uk/books?id=rY2IPwAACAAJ
  55. Preston, J., & Bishop, M. (2002). Views into the Chinese room: New essays on Searle and artificial intelligence. Clarendon Press. Retrieved from https://books.google.co.uk/books?id=0V6PwUrH2aYC
  56. Pyragas, K., & Pyragienė, T. (2008). Coupling design for a long-term anticipating synchronization of chaos. Physical Review E, 78, 046217.  https://doi.org/10.1103/PhysRevE.78.046217.CrossRefGoogle Scholar
  57. Pyragienė, T., & Pyragas, K. (2013). Anticipating spike synchronization in nonidentical chaotic neurons. Nonlinear Dynamics, 74(1), 297–306.CrossRefGoogle Scholar
  58. Pyragienė, T., & Pyragas, K. (2017). Anticipatory synchronization via low-dimensional filters. Physics Letters A, 381(22), 1893–1898.  https://doi.org/10.1016/j.physleta.2017.04.005.CrossRefGoogle Scholar
  59. Repp, B. (2005). Sensorimotor synchronization. A review of the tapping literature. Psychonomic Bulletin Review, 12(6), 969–992.CrossRefGoogle Scholar
  60. Rosen, R. (2012). Anticipatory systems. In Anticipatory systems (pp. 313–370). New York: Springer.CrossRefGoogle Scholar
  61. Seligman, M. E., Railton, P., Baumeister, R. F., & Sripada, C. (2013). Navigating into the future or driven by the past. Perspectives on Psychological Science, 8(2), 119–141.CrossRefGoogle Scholar
  62. Seoane, L. F., & Solé, R. V. (2018). Information theory, predictability and the emergence of complex life. Open Science, 5(2).  https://doi.org/10.1098/rsos.172221.
  63. Spencer, M. C., Roesch, E. B., Nasuto, S. J., Tanay, T., & Bishop, J. M. (2013, March). Abstract platforms of computation. In Aisb 2013 (pp. 25–32). Exeter. Retrieved from http://centaur.reading.ac.uk/35696/. (Published in: The 6th AISB symposium on computing and philosophy: The scandal of computation – What is computation? Mark Bishop and Yasemin J. Erden (editors) AISB Convention 2013, University of Exeter, 3–5 April 2013. Published by The Society for the Study of Artificial Intelligence and the Simulation of Behaviour. http://www.aisb.org.uk. ISBN: 9781908187314).
  64. Stepp, N. (2009). Anticipation in feedback-delayed manual tracking of a chaotic oscillator. Experimental Brain Research, 198, 521.CrossRefGoogle Scholar
  65. Stepp, N., & Frank, T. (2009). A data-analysis method for decomposing synchronization variability of anticipatory systems into stochastic and deterministic components. The European Physical Journal B-Condensed Matter and Complex Systems, 67(2), 251–257.CrossRefGoogle Scholar
  66. Stepp, N., & Turvey, M. (2010). On strong anticipation. Cognitive Systems Research, 11(2), 148–164.  https://doi.org/10.1016/j.cogsys.2009.03.003.CrossRefGoogle Scholar
  67. Sterling, P. (2012). Allostasis: A model of predictive regulation. Physiology & Behavior, 106(1), 5–15.  https://doi.org/10.1016/j.physbeh.2011.06.004. (Allostasis and Allostatic Load).CrossRefGoogle Scholar
  68. Stewart, J., Gapenne, O., & Di Paolo, E. (2010). Enaction: Toward a new paradigm for cognitive science. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  69. Synofzik, M., Vosgerau, G., & Newen, G. (2007). Beyond the comparator model: A multifactorial two-step account of agency. Consciousness and Cognition, 17(1), 219–239.CrossRefGoogle Scholar
  70. Thompson, E. (2010). Mind in life: Biology, phenomenology, and the sciences of mind. Cambridge, MA: Harvard University Press.Google Scholar
  71. van Boxtel, G. J., & Böcker, K. B. (2004). Cortical measures of anticipation. Journal of Psychophysiology, 18(2–3), 61–76.  https://doi.org/10.1027/0269-8803.18.23.61.CrossRefGoogle Scholar
  72. Vernon, D., Beetz, M., & Sandini, G. (2015). Prospection in cognition: The case for joint episodic-procedural memory in cognitive robotics. Frontiers in Robotics and AI, 2, 19.  https://doi.org/10.3389/frobt.2015.00019. Retrieved from https://www.frontiersin.org/article/10.3389/frobt.2015.00019
  73. Voss, H. U. (2000). Anticipating chaotic synchronization. Physical Review E, 61, 5115–5119.  https://doi.org/10.1103/PhysRevE.61.5115.CrossRefGoogle Scholar
  74. Voss, H. U. (2001). Dynamic long-term anticipation of chaotic states. Physical Review Letters, 87, 014102.  https://doi.org/10.1103/PhysRevLett.87.014102.CrossRefGoogle Scholar
  75. Voss, H. U. (2016a). The leaky integrator with recurrent inhibition as a predictor. Neural Computation, 28, 1498.CrossRefGoogle Scholar
  76. Voss, H. U. (2016b). Signal prediction by anticipatory relaxation dynamics. Physical Review E, 93(3), 030201.  https://doi.org/10.1103/PhysRevE.93.030201.CrossRefGoogle Scholar
  77. Voss, H. U. (2016c). A simple predictor based on delay-induced negative group delay. ArXiv e-prints.Google Scholar
  78. Voss, H. U. (2018). A delayed-feedback filter with negative group delay, Chaos: An Interdisciplinary Journal of Nonlinear Science 28, 113113.Google Scholar
  79. Ward, L., & Press, M. (2002). Dynamical cognitive science. MIT Press. Retrieved from https://books.google.co.uk/books?id=g1ZMAoWGYesC
  80. Wiener, N., & Schadé, J. (1963). Introduction to neurocybernetics. In N. Wiener & J. Schadé (Eds.), Progress in Brain Research, 2, p. 1–7. Elsevier.  https://doi.org/10.1016/S0079-6123(08)62055-5. Retrieved from http://www.sciencedirect.com/science/article/pii/S0079612308620555
  81. Wiggins, S. (2006). Introduction to applied nonlinear dynamical systems and chaos. New York: Springer. Retrieved from https://books.google.co.uk/books?id=YhXnBwAAQBAJ

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Division of Biomedical Sciences and Biomedical Engineering, School of Biological SciencesUniversity of ReadingReadingUK

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