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References

  1. https://www.pinterest.com/pin/427349452116437357/

  2. http://genies-de-linformatique.pipangaille.fr/AlanTuring.html

  3.  M. ABADI AND D. G. ANDERSEN, Learning to protect communications with adversarial neural cryptography, (2016). 

  4.  T. H. ABRAHAM, (physio)-logical circuits: the intellectual origins of the mcculloch-pitts neural networks, J Hist Behav Sci, 38 (2002), pp. 3–25. 

  5.  E. D. ADRIAN, The all-or-none principle in nerve, (1914), pp. 461–474.  OF THERMODYNAMICS - Memoirs by Carnot, Clausius and Thomson, HARPER and Brothers Publisher, London, 1899, ch. 3, pp. 65–107.

  6.  M. G. ALAN B. TICKLE, ROBERT ANDREWS AND J. DIEDERICH, The truthwillcometolight:Directionsandchallengesinextractingtheknowledge embeddedwithin trained artificial neural networks, (1998). 

  7.  M. W. AMES, J.S., THE SECOND LAW OF THERMODYNAMICS Memoirs by Carnot, Clausius and Thomson, HARPER and Brothers Publisher, London, 1899, ch. 3, pp. 65–107. 

  8.  N. D. ANNE SCHLOTTMANN, ELIZABETH D. RAY AND A. MITCHELL, Perceived physical and social causality in animated motions: Spontaneous reports and ratings, Acta Psychologica, 123 (2006), pp. 112–143. 

  9.  C. B. B. GORMAN, C. THURAU AND M. HUMPHRYS, elievability testing and bayesian imitation in interactive computer games, in Proc. Int. Conf. Simul. Adapt. Behav., 2006, pp. 655–666. 

  10.  BATES.J, Significance of information theory to neurophysiology, Transactions of the IRE Professional Group on Information Theory, 1 (Feb. 1953), pp. 137 – 142. 

  11. A. BEN-NAIM, A Farewell to Entropy: Statistical Thermodynamics Based on Information, World Scientific, 5 Toh Tuck Link,Singapore 596224, 2008. 

  12.  I. BENGTSSON AND K. ZYCZKOWSKI, Geometry of Quantum States - An Introduction to Quantum Entanglement, Cambridge University Press, The Edinburgh Building, Cambridge CB2 2RU, UK, 2006. 

  13.  E. C. BERKELEY, Giant brains; or, Machines that think, Wiley, New York, 1949. 

  14. B. E. BOSER, I. M. GUYON, AND V. N. VAPNIK, A training algorithm for optimal margin classifiers, in Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT ’92, New York, NY, USA, 1992, ACM, pp. 144–152. 

  15.  BOUTON,Restoringcorticalcontroloffunctionalmovementinahumanwith quadriplegia., (2016). 

  16.  J. B. T. BRENDEN M. LAKE, TOMER D. ULLMAN AND S. J. GERSHMAN, Building machines that learn and think like people, arXiv preprint, (2016). 

  17. R. S. BRENDEN M. LAKE AND J. B. TENENBAUM, Human-level concept learning through probabilistic program induction, Science, 350 (2015), pp. 1332–1338. 

  18.  M. BUNGE, A general black box theory, Chicago journals, (1963), pp. 346– 358.

  19. G. T. BURACAS, A. M. ZADOR, M. R. DEWEESE, AND T. D. ALBRIGHT, Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex, Neuron, 20 (1998), pp. 959–969. 

  20.  V. BUSH,Aswemaythink,TheAtlanticMonthly,176(1945),pp.101–108. 

  21.  R. C, Television comes to the home, Radio News, (1928), p. 1098.

  22.  W. B. CANNON, Henry pickering bowditch, (1922), pp. 183–195.

  23.  C. CERCIGNANI, Ludwig Boltzmann - The Man Who Trusted Atoms, Oxford University Press, Oxford, 1998. 

  24.  A. E. CHARLES C. KEMP AND E. TORRES-JARA, Challenges for robot manipulation in human environments, IEEE Robotics and Automation Magazine, (2007). 

  25.  R. CHURCH, Timing and time perception, Academy of sciences, (1984), pp. 566–582. 

  26.  I. CLOETE AND E. J. M. ZURADA, Knowledge-basedneurocomputing in medicine, Artificial Intelligence in Medicine, (2003).

  27.  B. COPELAND AND C. J. POSY, Computability - Turing, G¨odel, Church, and Beyond, The M.I.T. Press, Massachusetts Institute of Technology, 2013. 

  28.  J. CULBERTSON, Consciousness and behavior, Dubuque, Iowa: Wm. C. Brown, 1950. 

  29.  M. DAVIS, Engines of Logic, Mathematicians and the Origin of the Computer, W.W. Norton & Company, 2000.

  30.  J. DE BARENNE AND W. MCCULLOCH, Functional boundaries in the sensori-motor cortex of the monkey, Proceedings of the Society for Experimental Biology and Medicine, 35 (1936), pp. 329–331. 

  31.  Functional organization in the sensory cortex of the monkey (macaca mulatta), Journal of Neurophysiology, 1 (1938), pp. 69–85.

  32.  R. C. DEO, Machine learning in medicine, Basic Science for Clinicians, (2015). 

  33.  R. J. DOLAN AND P. DAYAN, Goals and habits in the brain, Neuron, 80 (2013), pp. 312–325. 

  34.  Y. L. DU-YIH TSAI AND E. MATSUYAM, Information entropy measure for evaluation of image quality, J Digit Imaging, (2008). 

  35.  M.-F. EHRLICH AND M. DELAFOY, La mmoire de travail: structure fonctionnement, capacit, L’anne psychologique, 90 (1990), pp. 403–428.

  36.  G. G. ELIZABETH S. SPELKE AND G. V. DE WALLE, The development of object perception, Visual cognition: An invitation to cognitive science, 2 (1995), pp. 297–330. 

  37.  N. E. M. ERIK G. MILLER AND P. A. VIOLA, Learning from one example throughshareddensitiesontransformations,inProceedingsoftheIEEEConference on Computer Vision and Pattern Recognition, 2017. 

  38.  D. E. ET AL, The volume clock: Insights into the high-frequency paradigm, The Journal of Portfolio Management, 39 (2012), pp. 19–29. 

  39.  R. FANO, Transmission of information, Tech. Rep. 65, Research Laboratory of Electronics at MIT, Cambridge, 1949.

  40.  R. M. FANO, Transmission of information: A statistical theory of communication, The M.I.T. Press, 1961. 

  41.  C. A. FRANCISCO LOPEZ-MUNOZ, JESUS BOYA, Neuron theory, the cornerstone of neuroscience, on the centenary of the nobel prize award to santiago ramon y cajal, Brain Research Bulletin, (2006), pp. 391–405. 

  42.  R. K. FRANKLIN ALLEN,Usinggeneticalgorithmstofindtechnicaltrading rules, Journal of Financial Economics, 51 (1999), pp. 245–271. 

  43. G. U. G. DAVID FOMEY, Modulation and coding for linear gaussian channels, IEEE TRANSACTIONS ON INFORMATION THEORY, 44 (1998), pp. 2384–2415. 

  44.  T. N. G. NAGY AND S. RICE, Document recognition and retrieval, in SPIE Proceedings, vol. 3967, 2000, pp. 58–69. 

  45.  A. N. L. GELY P. BASHARIN AND V. A. NAUMOV, The life and work of A.A. Markov, Elsevier, (2003), pp. 3–26. 

  46.  K. G¨ODEL, ¨uber formal unentscheidbare S¨atze der Principia Mathematica und verwandter Systeme i, Tech. Rep. 38, Monatshefte f¨ur Mathematik und Physik, Wien, 1931. p.173-198. 

  47.  J. W. HALLEY, Statistical Mechanics, From First Principles to Macroscopic Phenomena, Cambridge University Press, 2007. 

  48.  R. HARTLEY, Transmission of information, The Bell System Technical Journal, (July 1928). 

  49.  F. H. HINSLEY AND A. STRIPP, Codebreakers: The Inside Story of Bletchley Park Couverture, Oxford University Press, 2001. 

  50.  P. HITT, Manual for the solution of military ciphers, Army Service School Press, Fort Leavenworth, Kansas, (1916), p. 7. 

  51.  S. HLADKY AND V. BULITKO, An evaluation of models for predicting opponentlocationsinfirst-personshootervideogames,inProc.IEEESymp. Comput. Intell. Games, Perth, W.A., Australia, 2008. 

  52.  L. N. HOANG, Shannon’s information theory. http://www.science4all.org/article/ shannons-information-theory/, 2016. 

  53.  A. L. HODGKIN AND A. F. HUXLEY, A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology, (1952), pp. 500–544. 

  54.  J. HUBER AND R. FISCHER,Ontheimpactofinformationtheoryontoday’s communication technology, University of Erlangen-Nuremberg (FAU), Germany, (2006). 

  55.  C. A. D. III, Weather and forecasting, Weather Forecasting by Humans – Heuristics and Decision Making, 19 (2004), pp. 1115–1126.

  56.  A. IMBERT, Mode de fonctionnement conomique du cerveau, L’anne psychologique, 22 (1920), pp. 221–223. 

  57.  J. L. C. J. M. BENITEZ AND I. REQUENA, Are artificial neural networks black boxes?, (1997). 

  58. M. T. JAN PETERS, Robotics challenges for machine learning, (2007).

  59. E. JAYNES, Information theory and statistical mechanics, The Physical Review, 106 (1957), pp. 620–630. 

  60.  Gibbs vs boltzmann entropies, American Journal of Physics, (1964).

  61.  I. M. J. JOHN M. WOZENCRAFT, Principles of Communication Engineering, JOHN WILEY and SONS, Oxford, 1965. 

  62.  V. A. JUAN MANUEL FERNANDEZ MONTENEGRO, Cognitive evaluation for the diagnosis of alzheimer’s disease based on turing test and virtual environments, Physiology & Behavior, 173 (2007), pp. 42–51. 

  63.  S. KLEENE, On notation for ordinal numbers, The Journal of Symbolic Logic, 3 (1938), pp. 150–155.

  64.  S. KLEENE,Representationofeventsinnervenetsandfiniteautomata,InC. E. Shannon & J. McCarthy (Eds.), Automata studies, (1956), pp. 3–41. 

  65.  S. C. KLEENE,Representationofeventsinnervenetsandfiniteautomata,in Automata Studies, C. Shannon and J. McCarthy, eds., Princeton University Press, Princeton, NJ, 1956, pp. 3–41.

  66.  M. J. KLEIN, Max Planck and the Beginnings of the Quantum Theory, tech. rep., Department of Physics, Case Institute of Technology, Cleveland, Ohio, 1961.

  67. Y. LECUN, B. BOSER, J. S. DENKER, D. HENDERSON, R. E. HOWARD, W. HUBBARD, AND L. D. JACKEL,Backpropagationappliedtohandwritten zip code recognition, Neural Comput., 1 (1989), pp. 541–551. 

  68.  A. L.HODGKIN,Evidenceforelectricaltransmissioninnerve.part1,(1937), pp. 183 – 210. 

  69.  L. W. LICHTY AND M. C. TOPPING, American Broadcasting: A Source Book on the History of Radio and Television, Hastings House, 1975. 

  70.  M. B. LUIS VON AHN AND J. LANGFORD, Telling humans and computers part automatically, in ACM 47, 2004, pp. 56–60. 

  71.  J. L. MASSEY, Deep-space communications and coding: A marriage made in heaven, tech. rep., Signal and Information Processing Laboratory, Swiss Federal Institute of Technology, CH-8092 Z¨urich, Switzerland, 1992. 

  72.  MATHWORKS, Create, train, and simulate shallow and deep learning neural networks. https://nl.mathworks.com/products/neural-network.html. Training Algorithms.

  73.  W. MCCULLOCH,Whythemindisinthehead.,L.A.Jeffress(Ed.),Cerebral mechanisms in behavior, (1951), pp. 42–111. 

  74.  W. MCCULLOCH AND W. PITTS, A logical calculus of the ideas immanent innervousactivity,BulletinofMathematicalBiophysics,5(1943),pp.115– 133. 

  75.  J. S. K. E. T. MENGSEN ZHANG, GUILLAUME DUMAS, Enhanced emotional responses during social coordination with a virtual partner, International Journal of Psychophysiology, 104 (2016), pp. 33–43. 

  76.  M. MINSKY, Some universal elements for finite automata, In C. E. Shannon & J. McCarthy (Eds.), Automata studies, (1956), pp. 117–128. 

  77.  J. MOKYR, The second industrial revolution, 1870-1914, tech. rep., Northwestern University, 2003 Sheridan Rd., Evanston IL 60208, 1998.

  78.  S. H. NA, S.-H. JIN, S. Y. KIM, AND B.-J. HAM, Eeg in schizophrenic patients: mutual information analysis., Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 113 12 (2002), pp. 1954–60. 

  79.  NASA,Nasaspin-offdatabase.https://spinoff.nasa.gov/database/?k=telecommunication. 

  80.  Y. N. NATHANIEL D. DAW AND P. DAYAN, Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control, Nature Neuroscience, 8 (2005), pp. 1704–1711.

  81.  J. V. NEUMANN, Mathematische Grundlagen der Quantenmechanik, Springer Verlag, Berlin, 1932. 

  82.  C. N.S., 1943.

  83.  H. NYQUIST, Certain factors affecting telegraph speed, Bell System Technical Journal, (April 1924), pp. 324–346.

  84.  G. O’REGAN, Introduction to the History of Computing, Springer, 2016.

  85.  OUTBRAIN, Understanding the transmission of nerve impulses. http://www.dummies.com/education/science/understanding-thetransmission-of-nerve-impulses/. 

  86. H. P., A turing test for computer game bots, in IEEE Transactions on Computational Intelligence and AI in Games, 2009.

  87.  R. PA, Neuronal excitability: voltage-dependent currents and synaptic transmission, J Clin Neurophysiol, (1992), pp. 195–211.

  88.  W. F. PICKARD, Generalizations of the goldman-hodgkin-katz equation, Mathematical Biosciences, (1976), pp. 99–111. 

  89. W. PITTS, Some observations on the simple neuron circuit, The bulletin of mathematical biophysics, 4 (1942), pp. 121–129. 

  90. M. PLANCK, Ueber das gesetz der energieverteilung im normalspectrum, Deutsche Physikalische Gesellschaft, (1900). 

  91.  L. PRIZE, Home page of the loebner prize. http://www.loebner.net/Prizef/loebner-prize.html. 

  92.  J. QUINLAN, Induction of decision trees, Machine Learning, 1 (1986), pp. 81–106. 

  93.  J. R. QUINLAN, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993. 

  94. A. R., Behavior-Based Robotic, MIT Press, Cambridge, 1998. 

  95.  N. RASHEVSKY, Mathematical biophysics and psychology., Psychometrika, (1936), pp. 1–26. 

  96.  N. RASHEVSKY,Mathematicalbiophysics,Chicago:Univer.ChicagoPress, 1938. 

  97. A.-C. RATTAT AND S. DROIT-VOLET, Long-term memory for duration: Functioning and development, Psychologie franaise, 50 (2005), pp. 99–116. 

  98.  L. J. RIPS AND S. J. HESPOS, Divisions of the physical world: Concepts of objects and substances, Psychological Bulletin, 141 (2015), pp. 786–811.

  99. J. D. ROBERT ANDREWS AND A. B. TICKLE, Survey and critique of techniquesforextractingrulesfromtrainedartificialneuralnetworks,(1995).

  100.  F. ROSENBLATT, The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review, 65 (1958), pp. 386–408. 

  101.  F. ROSENBLATT, The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review, 65 (1958), pp. 386–408. 

  102.  T. ROSS, Machines that think, Scientific American, (1933), pp. 206–208. 

  103. H. RUMELHART AND WILLIAMS, Learning representations by back-propagating errors, Nature, 323 (1986), pp. 533 – 536. 

  104.  W. A. H. RUSHTON, The effect upon the threshold for nervous excitation of the length of nerve exposed, and the angle between current and nerve., (1927), pp. 357–377. 

  105.  SHANNON, The bandwagon, Institute of Radio Engineers, Transactions on Information Theory, (March 1956). 

  106.  C. SHANNON, Bell System Technical Journal, (1948). 

  107.  Communication in the presence of noisea mathematical theory of communication, Proceedings of the IEEE, 86 (1998). 

  108. M. SHER, Error-control coding in satellite communication, tech. rep., Department of Computer Science, International Islamic University Islamabad, Pakistan, 2002.

  109.  P. SMITH, An Introduction to G¨odel’s Theorems, Cambridge University Press, 2013. 2nd edition. 

  110.  E. S. SPELKE AND K. D. KINZLER, Core knowledge, Developmental Science, 10 (2007), pp. 89–96. 

  111.  N. SRIVASTAVA, G. HINTON, A. KRIZHEVSKY, I. SUTSKEVER, AND R. SALAKHUTDINOV, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, 15 (2014), pp. 1929–1958.

  112.  V. K. TATAI AND R. R. GUDWIN, Using a semiotics-inspired tool for the control of intelligent opponents in computer games, in Proc. IEEE Int. Conf. Integr. Knowl. Intensive Multi-Agent Syst., Cambridge, 2003. 

  113.  R. TOLMAN,PrinciplesofStatisticalMechanics,JOHNWILEYandSONS, Oxford, 1938. 

  114.  M. TRIBUS AND E. C. MCIRVINE, Energy and information, The Scientific American, (1971), pp. 179 – 184. 

  115.  A. M. TURING, On computable numbers, with an application to the Entscheidungsproblem, Proceedings of the London Mathematical Society, 2 (1936), pp. 230–265. 

  116.  A. M. TURING, Computing machinery and intelligence, Mind, 59 (1950), pp. 433–460.

  117.  A. A.-B. VERONICA BOLN-CANEDO, BEATRIZ REMESEIRO AND A. CAMPILHO, Machine learning for medical applications, (2016). 

  118. T. J. VONEIDA, Investigating the brain, (1962).

  119. S. A. WARD AND R. H. HALSTEAD, Computation structures, MIT Press, (1990).

  120.  J. WAXMAN, Information theory and neuroscience, (2009).

  121.  S. WERMTER AND R. SUN, An overview of hybrid neural systems, (2000). 

  122. K. P. WERRELL, The evolution of the cruise missile, Maxwell Air Force Base, Alabama: Air University Press, (1985).

  123. A. WHITEHEAD AND B. RUSSELL, Principia Mathematica, no. vol. 2 in Principia Mathematica, University Press, 1912. 

  124.  N. WIENER, Cybernetics or control and communication in the animal and the machine, (1950). 

  125.  S. L. WILLIAM E. RYAN,ChannelCodes:ClassicalandModern,Cambridge University Press, 2009. 

  126.  Q. S. Z. MINLI, Research on the application of artificial neural networks in tender offer for construction projects, Physics Procedia, (2012), pp. 1781 – 1788.

  127.  S. ZALOGA, V-1 Flying Bomb 1942 - 52, Osprey Publishing, Oxford, UK, 2005. 

  128.  M. ZIAD OBERMEYER AND E. J. EMANUEL,Predictingthefuture bigdata, machine learning, and clinical medicine, (2016). 

  129. M. H. H. V. H. M. L. ZIYU WANG, TOM SCHAUL AND N. DE FREITAS, Dueling network architectures for deep reinforcement learning, arXiv preprint, (2016).

References of the home page picture

HISTORY

Science                          https://www.pinterest.com/pin/510947520203946007/

Technology                [52]

Cybernetics               https://www.pinterest.com/pin/341429215480983870/ 

Information                   http://www.careeraddict.com/zombie-tech-that-refuses-to-die     

THREE LANDMARK PAPERS

Turing                              http://fr.ubergizmo.com/wp-content/uploads/2014/06/Dr-Alan-Turing-2956483.jpg

Shannon                          http://history-computer.com/ModernComputer/thinkers/Shannon.html

McCulloch&Pitts            http://nautil.us/issue/21/information/the-man-who-tried-to-redeem-the-world-with-logic

                                          http://cyberneticians.com/THSH3/T4.html

IMPACTS

Brain circuit                   https://www.scan.co.uk/3xs/info/what-is-deep-learning

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