Trajectories-State: A New Neural Mechanism to Interpretate Cerebral Dynamics

  • Sergio Miguel-Tomé
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9107)


With regard to neural networks, there are two different areas which have generated two lines of research. One research interest comes from the field of computer science which seeks to create and design neural networks capable of performing computational tasks. In this line of research, any neural network is relevant because the important issue is the problems which they are capable of resolving. Thus, neural networks are computational devices and computational power and the computational process which they perform are researched. The other interest of research is related to neuroscience. This focuses on both neural and brain activity. The big difference between these two lines of research can be observed from the outset. In the first, the neural network is designed and its performance on computational tasks is then researched. In the second, performance on computational tasks is known but the neural mechanism is not and neuroscience seeks to identify it. An interaction between these two lines of research is very positive because it produces synergies which generate important advances in both lines of research e.g. Hopfield’s networks. This article enunciates a neural mechanism to interpret neural dynamics based on some of the results produced by computer science. This mechanism identifies an internal or external state s with a formal language L. Independently, if the mechanism exist or not in the human brain, this mechanism can be used to design new architectures for neural networks.


Neural Network Neural Mechanism Auditory Cortex Formal Language Computational Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Amari, S.-I.: Neural theory of association and concept-formation. Biological Cybernetics 26(3), 175–185 (1977)CrossRefzbMATHGoogle Scholar
  2. 2.
    Amit, D.J.: Modeling Brain Function: The World of Attractor Neural Networks. Cambridge University Press (1992)Google Scholar
  3. 3.
    Bathellier, B., et al.: Discrete neocortical dynamics predict behavioral categorization of sounds. Neuron 76(2), 435–449 (2012)CrossRefGoogle Scholar
  4. 4.
    Gray, C.M., et al.: Synchronization of oscillatory neuronal responses in cat striate cortex: Temporal properties. Visual Neuroscience 8, 337–347 (1992)CrossRefGoogle Scholar
  5. 5.
    Hirsch, M.W.: Convergent activation dynamics in continuous time networks. Neural Networks 2(5), 331–349 (1989)CrossRefGoogle Scholar
  6. 6.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences 79(8), 2554–2558 (1982)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Joliot, M., Ribary, U., Llinás, R.: Human oscillatory brain activity near 40 hz coexists with cognitive temporal binding. Proceedings of the National Academy of Sciences 91(24), 11748–11751 (1994)CrossRefGoogle Scholar
  8. 8.
    Kenet, T., et al.: Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956 (2003)CrossRefGoogle Scholar
  9. 9.
    Kohonen, T.: Associative Memory-A System Theoretical Approach. Springer (1978)Google Scholar
  10. 10.
    Kolen, J.F.: Fool’s gold: Extracting finite state machines from recurrent network dynamics. In: Advances in Neural Information Processing Systems, vol. 6, pp. 501–508. Morgan Kaufmann (1994)Google Scholar
  11. 11.
    Llinás, R.R., et al.: Gamma-band deficiency and abnormal thalamocortical activity in p/q-type channel mutant mice. Proceedings of the National Academy of Sciences 104(45), 17819–17824 (2007)CrossRefGoogle Scholar
  12. 12.
    Llinás, R.: The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242(4886), 1654–1664 (1988)CrossRefGoogle Scholar
  13. 13.
    Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Henry Holt and Co., Inc., New York (1982)Google Scholar
  14. 14.
    McCulloch, W., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics (5), 115–133 (1943)Google Scholar
  15. 15.
    Meyers, E.M., Freedman, D.J., Kreiman, G., Miller, E.K., Poggio, T.: Dynamic population coding of category information in inferior temporal and prefrontal cortex. Journal of Neurophysiology 100(3), 1407–1419 (2008)CrossRefGoogle Scholar
  16. 16.
    Minsky, M.L.: Computation: Finite and Infinite Machines. Prentice-Hall, Inc. (1967)Google Scholar
  17. 17.
    Mira, J., Delgado, A.E.: Where is knowledge in robotics? some methodological issues on symbolic and connectionist perspectives of AI. In: Zhou, C., Maravall, D., Ruan, D., Kacprzyk, J. (eds.) Autonomous Robotic Systems, pp. 3–34 (2003)Google Scholar
  18. 18.
    Mira, J., Delgado, A.: Neural modeling in cerebral dynamics. Biosystems 71(1-2), 133–144 (2003)CrossRefGoogle Scholar
  19. 19.
    Mira, J.M., García, A.E.: On how the computational paradigm can help us to model and interpret the neural function. Natural Computing 6(3), 211–240 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Newell, A.: The knowledge level. AI Magazine 2(2), 1–33 (1981)MathSciNetGoogle Scholar
  21. 21.
    Omlin, C.: Understanding and Explaining DRN Behavior. In: Field Guide to Dynamical Recurrent Networks, pp. 207–227. Wiley-IEEE Press (2001)Google Scholar
  22. 22.
    Pepperberg, I.: Talking with alex: Logic and speech in parrots. Scientific American 9(4), 60–65 (1998)Google Scholar
  23. 23.
    Polack, C., McConnell, B., Miller, R.: Associative foundation of causal learning in rats. Learning and Behavior 41(1), 25–41 (2013)CrossRefGoogle Scholar
  24. 24.
    Sekar, K., et al.: Cortical response tracking the conscious experience of threshold duration visual stimuli indicates visual perception is all or none. Proceedings of the National Academy of Sciences 110(14), 5642–5647 (2013)CrossRefGoogle Scholar
  25. 25.
    Stosiek, C., et al.: In vivo two-photon calcium imaging of neuronal networks. Proceedings of the National Academy of Sciences 100(12), 7319–7324 (2003)CrossRefGoogle Scholar
  26. 26.
    Tsuda, I.: Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems. Behavioral and Brain Sciences 24(5), 793–810 (2001)CrossRefGoogle Scholar
  27. 27.
    Tsuda, I.: Hypotheses on the functional roles of chaotic transitory dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science 19(1), 15113 (2009)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Zimmerman, H., Neuneier, R.: Neural Network Architectures for the Modeling of Dynamic Systems. In: A Field Guide to Dynamical Recurrent Networks, pp. 311–350. Wiley-IEEE Press (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Grupo de Investigación en Minería de Datos (MiDa)Universidad de SalamancaSalamancaSpain

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