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References    217




                  [17] R. Kozma, H. Aghazarian, T. Huntsberger, E. Tunstel, W.J. Freeman, Computational as-
                      pects of cognition and consciousness in intelligent devices, IEEE Computational Intel-
                      ligence Magazine 2 (3) (2007) 53e64.
                  [18] R.A. Brooks, Cambrian Intelligence: The Early History of the New AI (vol. 97), MIT
                      press, Cambridge, MA, 1999.
                  [19] G.G. Towell, J.W. Shavlik, Knowledge-based artificial neural networks, Artificial
                      Intelligence 70 (1994) 119e165.
                  [20] P. Taylor, J.N. Hobbs, J. Burroni, H.T. Siegelmann, The global landscape of cognition:
                      hierarchical aggregation as an organizational principle of human cortical networks and
                      functions, Scientific Reports 5 (2015) 18112.
                  [21] W.S. McCulloch, W. Pitts, A logical calculus of the idea immanent in nervous activity,
                      Bulletin of Mathematical Biophysics 5 (1943) 115e133.
                  [22] N. Wiener, Cybernetics: Control and Communication in the Animal and the Machine,
                      Wiley, New York, 1948, p. 194.
                  [23] A.N. Kolmogorov, On the representation of continuous functions of many variables by
                      superposition of continuous functions of one variable and addition, Doklady Akademii
                      Nauk USSR 144 (1957) 679e681. American Mathematical Society Translation, 28,
                      55e59, 1963.
                  [24] V.I. Arnold, On functions of three variables, Doklady Akademii Nauk USSR 114 (1957)
                      679e681.
                  [25] P.J. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behav-
                      ioral Sciences (Doctoral Dissertation, Applied Mathematics), Harvard University, MA,
                      1974.
                  [26] D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by error
                      propagation, in: D.E. Ru- melhart, J.L. McClelland, the PDP Research Group (Eds.),
                      Parallel Distributed Processing, vol. 1, MIT Press, Cambridge, MA, 1986, pp. 318e362.
                  [27] R. Hecht-Nielsen, Kolmogorov mapping neural network existence theorem, in: IEEE
                      First International Conference on Neural Networks, vol. 3, 1987, pp. 11e13.
                  [28] S. Grossberg, Nonlinear neural networks: principles, mechanisms, and architectures,
                      Neural Networks 1 (1) (1988) 17e61.
                  [29] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to
                      document recognition, Proceedings of the IEEE 86 (11) (1998) 2278e2324.
                  [30] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation 9 (8)
                      (1997) 1735e1780.
                  [31] Y. LeCun, Y. Bengio, G. Hinton, Deep learing, Nature 521 (7553) (2015) 436e444.
                  [32] W.J. Freeman, Proposed cortical “shutter” mechanism in cinematographic perception,
                      in: Neurodynamics of Cognition and Consciousness, Springer Berlin Heidelberg,
                      2007, pp. 11e38.
                  [33] R. Kozma, W.J. Freeman, Cognitive Phase Transitions in the Cerebral Cortex d
                      Enhancing the Neuron Doctrine by Modeling Neural Fields, Springer, New York, 2016.
                  [34] R. Kozma, W.J. Freeman, Cinematic operation of the cerebral cortex interpreted via
                      critical transitions in self-organized dynamic systems, Frontiers in Systems Neurosci-
                      ence 11 (2017).
                  [35] J.S. Kelso, Dynamic Patterns: The Self-organization of Brain and Behavior, MIT press,
                      1997.
                  [36] H. Mercier, D. Sperber, Why do humans reason? Arguments for an argumentative
                      theory, Behavioral and Brain Sciences 34 (2011) 57e111.
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