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The Twin Hypotheses

Brain Code and the Fundamental Code Unit: Towards Understanding the Computational Primitive Elements of Cortical Computing

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Advances in Artificial Intelligence and Its Applications (MICAI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8265))

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Abstract

The Brain Code (BC) relies on several essential concepts that are found across a range of physiological and behavioral functions. The Fundamental Code Unit (FCU) assumes an abstract code unit to allow for a higher order of abstractions that informs information exchanges at the cellular and genetic levels, together the two hypotheses provide a foundation for a system level understanding and potentially cyphering of the Brain Code [1–3]. This paper discusses an organizing principle for an abstract framework tested in a limited scope experimental approach as a means to show an empirical example of cognitive measurement as well as a framework for a Cortical Computation methodology. Four important concepts of the BC and FCU are discussed. First, the principle of activation based on Guyton thresholds. This is seen in the well-known and widely documented action potential threshold in neurons, where once a certain threshold is reached, the neuron will fire, reflecting the transmission of information. The concept of thresholds is also valid in Weber minimum detectable difference in our sensing, which applies to our hearing, seeing and touching. Not only the intensity, but also the temporal pattern is affected by this [4]. This brings insight to the second important component, which is duration. The combination of both threshold crossing and duration may define the selection mechanisms, depending on both external and intrinsic factors. However, ranges exist within which tuning can take place. Within reason it can be stated that no functional implication will occur beyond this range. Transfer of information and processing itself relies on energy and can be described in waveforms, which is the third concept. The human sensing system acts as transducer between the different forms of energy, the fourth principle. The aim of the brain code approach is to incorporate these four principles in an explanatory, descriptive and predictive model. The model will take into account fundamental physiological knowledge and aims to reject assumptions that are not yet fully established. In order to fill in the gaps with regards to the missing information, modules consisting of the previous described four principles are explored. This abstraction should provide a reasonable placeholder, as it is based on governing principles in nature. The model is testable and allows for updating as more data becomes available. It aims to replace methods that rely on structural levels to abstraction of functions, or approaches that are evidence-based, but across many noisy-elements and assumptions that outcomes might not reflect behavior at the organism level.

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References

  1. Howard, N.: Brain Language: The Fundamental Code Unit. The Brain Sciences Journal 1(1), 4–45 (2012)

    Google Scholar 

  2. Howard, N.: The Fundamental Code Unit of the Brain: Deciphering the DNA of Cognition. Frontiers Systems Neuroscience (commissioned, in preparation, 2013)

    Google Scholar 

  3. Howard, N., Rao, D., Fahlstrom, R., Bergmann, J., Stein, J.: The Fundamental Code Unit- Applying Neural Oscillation Detection Across Clinical Conditions. Frontiers (commissioned, in Preparation, 2013)

    Google Scholar 

  4. Howard, N., Rao, D., Fahlstrom, R., Stein, J.: The Fundamental Code Unit: A Framework for Biomarker Analysis. In: Proc. The 2nd Neurological Biomarkers Conference at the 2013 Biomarker Summit, San Francisco, California, March 20-22 (2013)

    Google Scholar 

  5. Fitzgibbons, P.J., Wightman, F.L.: Gap detection in normal and hearing-impaired listeners. J. Acoust. Soc. Am. 72(3), 761–765 (1982)

    Google Scholar 

  6. Rubinsztein, D.C.: The roles of intracellular protein-degradation pathways in neurodegeneration. Nature 443(7113), 780–786 (2006)

    Google Scholar 

  7. Howard, N., Bergmann, J., Stein, J.: Combined Modality of the Brain Code Approach for Early Detection and the Long-term Monitoring of Neurodegenerative Processes. Frontiers Special Issue INCF Course Imaging the Brain at Different Scales (2013)

    Google Scholar 

  8. Moradi, F., Liu, L.C., Cheng, K., Waggoner, R.A., Tanaka, K., Ioannides, A.A.: Consistent and precise localization of brain activity in human primary visual cortex by MEG and fMRI. Neuroimage 18(3), 595–609 (2003)

    Google Scholar 

  9. Ioannides, A.A., Fenwick, P.B.C., Liu, L.: Widely distributed magnetoencephalography spikes related to the planning and execution of human saccades. The Journal of Neuroscience 25(35), 7950–7967 (2005)

    Google Scholar 

  10. Ioannides, A.A., Corsi-Cabrera, M., Fenwick, P.B.C., del Rio Portilla, Y., Laskaris, N.A., Khurshudyan, A., Theofilou, D., Shibata, T., Uchida, S., Nakabayashi, T.: MEG tomography of human cortex and brainstem activity in waking and REM sleep saccades. Cerebral Cortex 14(1), 56–72 (2004)

    Google Scholar 

  11. Ioannides, A.A.: Dynamic functional connectivity. Current Opinion in Neurobiology 17(2), 161–170 (2007)

    Google Scholar 

  12. Ioannides, A.A., Liu, L., Poghosyan, V., Saridis, G.A., Gjedde, A., Ptito, M., Kupers, R.: MEG reveals a fast pathway from somatosensory cortex to occipital areas via posterior parietal cortex in a blind subject. Front. Hum. Neurosci. 7, 429 (2013)

    Google Scholar 

  13. Tzelepi, A., Ioannides, A.A., Poghosyan, V.: Early (N70m) neuromagnetic signal topography and striate and extrastriate generators following pattern onset quadrant stimulation. NeuroImage 13(4), 702–718 (2001)

    Google Scholar 

  14. Poghosyan, V., Ioannides, A.A.: Precise mapping of early visual responses in space and time. Neuroimage 35(2), 759–770 (2007)

    Google Scholar 

  15. Plomp, G., Liu, L., van Leeuwen, C., Ioannides, A.A.: The “mosaic stage” in amodal completion as characterized by magnetoencephalography responses. J. Cogn. Neurosci. 18(8), 1394–1405 (2006)

    Google Scholar 

  16. Liu, L.C., Plomp, G., van Leeuwen, C., Ioannides, A.A.: Neural correlates of priming on occluded figure interpretation in human fusiform cortex. Neuroscience 141(3), 1585–1597 (2006)

    Google Scholar 

  17. Bakar, A.A., Liu, L., Conci, M., Elliott, M.A., Ioannides, A.A.: Visual field and task influence illusory figure responses. Human Brain Mapping 29(11), 1313–1326 (2008)

    Google Scholar 

  18. Liu, L., Ioannides, A.A., Streit, M.: Single trial analysis of neurophysiological correlates of the recognition of complex objects and facial expressions of emotion. Brain Topography 11(4), 291–303 (1999)

    Google Scholar 

  19. Ioannides, A.A., Liu, L.C., Kwapien, J., Drozdz, S., Streit, M.: Coupling of regional activations in a human brain during an object and face affect recognition task. Human Brain Mapping 11(2), 77–92 (2000)

    Google Scholar 

  20. Ioannides, A.A., Poghosyan, V., Dammers, J., Streit, M.: Real-time neural activity and connectivity in healthy individuals and schizophrenia patients. Neuroimage 23(2), 473–482 (2004)

    Google Scholar 

  21. Ioannides, A.A., Kostopoulos, G.K., Liu, L., Fenwick, P.B.C.: MEG identifies dorsal medial brain activations during sleep. Neuroimage 44(2), 455–468 (2009)

    Google Scholar 

  22. Poghosyan, V., Ioannides, A.A.: Attention modulates earliest responses in the primary auditory and visual cortices. Neuron 58(5), 802–813 (2008)

    Google Scholar 

  23. Ioannides, A.A., Poghosyan, V.: Spatiotemporal dynamics of early spatial and category-specific attentional modulations. NeuroImage 60(3), 1638–1651 (2012)

    Google Scholar 

  24. Ioannides, A.A., Poghosyan, V., Liu, L., Saridis, G., Tamietto, M., de Beeck, M.O., De Tiège, X., Weiskrantz, L., De Gelder, B.: Spatiotemporal profiles of visual processing with and without primary visual cortex. Neuroimage (2012)

    Google Scholar 

  25. Marx, G., Gilon, C.: The molecular basis of memory. Part 2: chemistry of the tripartite mechanism. ACS Chem. Neurosci. 4(6), 983–993 (2013)

    Google Scholar 

  26. Marx, G., Gilon, C.: The Molecular Basis of Memory. ACS Chemical Neuroscience 3(8), 633–642 (2012)

    Google Scholar 

  27. Howard, N.: The Twin Hypotheses: Brain Code and the Fundamental Code Unit. Springer Lecture Notes in Artificial Intelligence (in press, 2013)

    Google Scholar 

  28. Ramon Cajal, S.: Histology of the nervous system of man and vertebrates. Oxford Univ. Press, New York (1995)

    Google Scholar 

  29. Koulakov, A.A., Chklovskii, D.B.: Orientation preference patterns in mammalian visual cortex: a wire length minimization approach. Neuron 29(2), 519–527 (2001)

    Google Scholar 

  30. Mitchison, G.: Neuronal branching patterns and the economy of cortical wiring. Proceedings of the Royal Society of London. Series B: Biological Sciences 245(1313), 151–158 (1991)

    Google Scholar 

  31. Gara, A., Blumrich, M.A., Chen, D., Chiu, G.L.T., Coteus, P., Giampapa, M.E., Haring, R.A., Heidelberger, P., Hoenicke, D., Kopcsay, G.V., Liebsch, T.A., Ohmacht, M., Steinmacher-Burow, B.D., Takken, T., Vranas, P.: Overview of the Blue Gene/L system architecture. IBM Journal of Research and Development 49(2.3), 195–212 (2005)

    Google Scholar 

  32. Rolfe, D.F., Brown, G.C.: Cellular energy utilization and molecular origin of standard metabolic rate in mammals. Physiological Reviews 77(3), 731–758 (1997)

    Google Scholar 

  33. Kety, S.S.: The general metabolism of the brain in vivo. Metabolism of the Nervous System, 221–237 (1957)

    Google Scholar 

  34. Sokoloff, L.: The metabolism of the central nervous system in vivo. Handbook of Physiology-Neurophysiology 3, 1843–1864 (1960)

    Google Scholar 

  35. Aiello, L.C., Bates, N., Joffe, T.: In defense of the expensive tissue hypothesis. In: Evolutionary Anatomy of the Primate Cerebral Cortex, pp. 57–78. Cambridge University Press, Cambridge (2001)

    Google Scholar 

  36. Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Annual Review of Neuroscience 24(1), 1193–1216 (2001)

    Google Scholar 

  37. Baddeley, A.: The central executive: A concept and some misconceptions. Journal of the International Neuropsychological Society 4(5), 523–526 (1998)

    Google Scholar 

  38. Balasubramanian, V., Kimber, D., Berry Ii, M.J.: Metabolically efficient information processing. Neural Computation 13(4), 799–815 (2001)

    MATH  Google Scholar 

  39. Field, D.J.: What is the goal of sensory coding? Neural Computation 6(4), 559–601 (1994)

    MathSciNet  Google Scholar 

  40. Levy, W.B., Baxter, R.A.: Energy efficient neural codes. Neural Computation 8(3), 531–543 (1996)

    Google Scholar 

  41. Fröhlich, F., McCormick, D.A.: Endogenous electric fields guide neocortical network activity. Neuron 67(1), 129–143 (2010)

    Google Scholar 

  42. Nave, O., Neuman, Y., Perlovsky, L., Howard, N.: How much information should we drop to become intelligent? Applied Mathematics and Computation (under review, 2013)

    Google Scholar 

  43. Howard, N.: Methods for Cortical Computing. IARPA RFI 13-05 Cortical Computing Primitives and Connectomics (submitted, 2013)

    Google Scholar 

  44. Howard, N.: Mathematical Review for Cortical Computation Proposition for Brain Code Hypothesis. Frontiers Systems Neuroscience (commissioned, in preparation 2013)

    Google Scholar 

  45. Anderson, M.L.: Neural reuse: A fundamental organizational principle of the brain. Behavioral and Brain Sciences 33(4), 245 (2010)

    Google Scholar 

  46. Busch, N.A., Dubois, J., VanRullen, R.: The phase of ongoing EEG oscillations predicts visual perception. The Journal of Neuroscience 29(24), 7869–7876 (2009)

    Google Scholar 

  47. Laughlin, S.B., Sejnowski, T.J.: Communication in Neuronal Networks. Science 301(5641), 1870–1874 (2003)

    Google Scholar 

  48. Shibata, K., Watanabe, T., Sasaki, Y., Kawato, M.: Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 334(6061), 1413–1415 (2011)

    Google Scholar 

  49. Sadaghiani, S., Hesselmann, G., Friston, K.J., Kleinschmidt, A.: The relation of ongoing brain activity, evoked neural responses, and cognition. Frontiers in Systems Neuroscience 4 (2010)

    Google Scholar 

  50. Wang, X.-J.: Decision making in recurrent neuronal circuits. Neuron 60(2), 215–234 (2008)

    Google Scholar 

  51. Barraclough, D.J., Conroy, M.L., Lee, D.: Prefrontal cortex and decision making in a mixed-strategy game. Nature Neuroscience 7(4), 404–410 (2004)

    Google Scholar 

  52. Arieli, A., Sterkin, A., Grinvald, A., Aertsen, A.: Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273(5283), 1868–1871 (1996)

    Google Scholar 

  53. Ploner, M., Lee, M.C., Wiech, K., Bingel, U., Tracey, I.: Prestimulus functional connectivity determines pain perception in humans. Proceedings of the National Academy of Sciences 107(1), 355–360 (2010)

    Google Scholar 

  54. Kayser, C., Montemurro, M.A., Logothetis, N.K., Panzeri, S.: Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron 61(4), 597–608 (2009)

    Google Scholar 

  55. Arnal, L.H., Giraud, A.-L.: Cortical oscillations and sensory predictions. Trends in Cognitive Sciences 16(7), 390–398 (2012)

    Google Scholar 

  56. Stefanics, G., Hangya, B., Hernádi, I., Winkler, I., Lakatos, P., Ulbert, I.: Phase entrainment of human delta oscillations can mediate the effects of expectation on reaction speed. The Journal of Neuroscience 30(41), 13578–13585 (2010)

    Google Scholar 

  57. SanMiguel, I., Widmann, A., Bendixen, A., Trujillo-Barreto, N., Schröger, E.: Hearing Silences: Human Auditory Processing Relies on Preactivation of Sound-Specific Brain Activity Patterns. The Journal of Neuroscience 33(20), 8633–8639 (2013)

    Google Scholar 

  58. Kok, P., Jehee, J.F., de Lange, F.P.: Less is more: expectation sharpens representations in the primary visual cortex. Neuron 75(2), 265–270 (2012)

    Google Scholar 

  59. de Lange, F.P., Rahnev, D.A., Donner, T.H., Lau, H.: Prestimulus oscillatory activity over motor cortex reflects perceptual expectations. The Journal of Neuroscience 33(4), 1400–1410 (2013)

    Google Scholar 

  60. Chavan, C.F., Manuel, A.L., Mouthon, M., Spierer, L.: Spontaneous pre-stimulus fluctuations in the activity of right fronto-parietal areas influence inhibitory control performance. Frontiers in Human Neuroscience 7 (2013)

    Google Scholar 

  61. Köver, H., Bao, S.: Cortical plasticity as a mechanism for storing Bayesian priors in sensory perception. PloS One 5(5), e10497 (2010)

    Google Scholar 

  62. Fiser, J., Berkes, P., Orbán, G., Lengyel, M.: Statistically optimal perception and learning: from behavior to neural representations. Trends in Cognitive Sciences 14(3), 119–130 (2010)

    Google Scholar 

  63. Drewes, J., VanRullen, R.: This is the rhythm of your eyes: the phase of ongoing electroencephalogram oscillations modulates saccadic reaction time. The Journal of Neuroscience 31(12), 4698–4708 (2011)

    Google Scholar 

  64. Schurger, A., Sitt, J.D., Dehaene, S.: An accumulator model for spontaneous neural activity prior to self-initiated movement. Proceedings of the National Academy of Sciences 109(42), E2904–E2913 (2012)

    Google Scholar 

  65. Fried, I., Mukamel, R., Kreiman, G.: Internally generated preactivation of single neurons in human medial frontal cortex predicts volition. Neuron 69(3), 548–562 (2011)

    Google Scholar 

  66. Soon, C.S., Brass, M., Heinze, H.-J., Haynes, J.-D.: Unconscious determinants of free decisions in the human brain. Nature Neuroscience 11(5), 543–545 (2008)

    Google Scholar 

  67. Engel, A.K., Fries, P., Singer, W.: Dynamic predictions: oscillations and synchrony in top–down processing. Nature Reviews Neuroscience 2(10), 704–716 (2001)

    Google Scholar 

  68. Hesselmann, G., Kell, C.A., Eger, E., Kleinschmidt, A.: Spontaneous local variations in ongoing neural activity bias perceptual decisions. Proceedings of the National Academy of Sciences 105(31), 10984–10989 (2008)

    Google Scholar 

  69. Boly, M., Balteau, E., Schnakers, C., Degueldre, C., Moonen, G., Luxen, A., Phillips, C., Peigneux, P., Maquet, P., Laureys, S.: Baseline brain activity fluctuations predict somatosensory perception in humans. Proceedings of the National Academy of Sciences 104(29), 12187–12192 (2007)

    Google Scholar 

  70. Gilaie-Dotan, S., Hahamy-Dubossarsky, A., Nir, Y., Berkovich-Ohana, A., Bentin, S., Malach, R.: Resting state functional connectivity reflects abnormal task-activated patterns in a developmental object agnosic. Neuroimage 70, 189–198 (2013)

    Google Scholar 

  71. Watkins, K.E., Cowey, A., Alexander, I., Filippini, N., Kennedy, J.M., Smith, S.M., Ragge, N., Bridge, H.: Language networks in anophthalmia: maintained hierarchy of processing in ‘visual’cortex’. Brain 135(5), 1566–1577 (2012)

    Google Scholar 

  72. Liu, J., Qin, W., Yuan, K., Li, J., Wang, W., Li, Q., Wang, Y., Sun, J., von Deneen, K.M., Liu, Y.: Interaction between dysfunctional connectivity at rest and heroin cues-induced brain responses in male abstinent heroin-dependent individuals. PloS One 6(10), e23098 (2011)

    Google Scholar 

  73. Fahn, S.: Description of Parkinson’s disease as a clinical syndrome. Annals of the New York Academy of Sciences 991(1), 1–14 (2003)

    Google Scholar 

  74. Jankovic, J.: Parkinson’s disease: clinical features and diagnosis. Journal of Neurology, Neurosurgery & Psychiatry 79(4), 368–376 (2008)

    Google Scholar 

  75. Aarsland, D., Brønnick, K., Ehrt, U., De Deyn, P.P., Tekin, S., Emre, M., Cummings, J.L.: Neuropsychiatric symptoms in patients with Parkinson’s disease and dementia: frequency, profile and associated care giver stress. Journal of Neurology. Neurosurgery & Psychiatry 78(1), 36–42 (2007)

    Google Scholar 

  76. Aarsland, D., Andersen, K., Larsen, J.P., Perry, R., Wentzel-Larsen, T., Lolk, A., Kragh-Sorensen, P.: The rate of cognitive decline in Parkinson disease. Archives of Neurology 61(12), 1906 (2004)

    Google Scholar 

  77. Aarsland, D., Larsen, J.P., Lim, N.G., Janvin, C., Karlsen, K., Tandberg, E., Cummings, J.L.: Range of neuropsychiatric disturbances in patients with Parkinson’s disease. Journal of Neurology, Neurosurgery & Psychiatry 67(4), 492–496 (1999)

    Google Scholar 

  78. Bottini Bonfanti, A.: More than movement: the importance of the evolution of mild cognitive impairment in Parkinson’s disease. Journal of Neurology, Neurosurgery & Psychiatry (2013)

    Google Scholar 

  79. Chaudhuri, K., Healy, D.G., Schapira, A.H.V.: Non-motor symptoms of Parkinson’s disease: diagnosis and management. The Lancet Neurology 5(3), 235–245 (2006)

    Google Scholar 

  80. de la Monte, S.M., Wells, S.E., Hedley-Whyte, E.T., Growdon, J.H.: Neuropathological distinction between Parkinson’s dementia and Parkinson’s plus Alzheimer’s disease. Annals of Neurology 26(3), 309–320 (1989)

    Google Scholar 

  81. Hu, M., Cooper, J., Beamish, R., Jones, E., Butterworth, R., Catterall, L., Ben-Shlomo, Y.: How well do we recognise non-motor symptoms in a British Parkinson’s disease population? J. Neurol. 258(8), 1513–1517 (2011)

    Google Scholar 

  82. Riedel, O., Klotsche, J., Spottke, A., Deuschl, G., Förstl, H., Henn, F., Heuser, I., Oertel, W., Reichmann, H., Riederer, P.: Cognitive impairment in 873 patients with idiopathic Parkinson’s disease. Journal of Neurology 255(2), 255–264 (2008)

    Google Scholar 

  83. Starkstein, S., Preziosi, T., Berthier, M., Bolduc, P., Mayberg, H., Robinson, R.: Depression and cognitive impairment in Parkinson’s disease. Brain: a Journal of Neurology 112, 1141–1153 (1989)

    Google Scholar 

  84. Wertman, E., Speedie, L., Shemesh, Z., Gilon, D., Raphael, M., Stessman, J.: Cognitive disturbances in parkinsonian patients with depression. Cognitive and Behavioral Neurology 6(1), 31–37 (1993)

    Google Scholar 

  85. Bavelier, D., Newport, E.L., Hall, M.L., Supalla, T., Boutla, M.: Persistent Difference in Short-Term Memory Span Between Sign and Speech Implications for Cross-Linguistic Comparisons. Psychological Science 17(12), 1090–1092 (2006)

    Google Scholar 

  86. Tsanas, A., Little, M.A., McSharry, P.E., Ramig, L.O.: Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. Journal of the Royal Society Interface 8(59), 842–855 (2011)

    Google Scholar 

  87. Tsanas, A., Little, M.A., McSharry, P.E., Spielman, J., Ramig, L.O.: Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Trans. Biomed. Eng. 59(5), 1264–1271 (2012)

    Google Scholar 

  88. Skodda, S., Grönheit, W., Schlegel, U.: Impairment of Vowel Articulation as a Possible Marker of Disease Progression in Parkinson’s Disease. PloS One 7(2), e32132 (2012)

    Google Scholar 

  89. Howard, N., Stein, J., Aziz, T.: Early Detection of Parkinson’s Disease from Speech and Movement Recordings. Oxford Parkinson’s Disease Center Research Day (2013)

    Google Scholar 

  90. Pham, Q.-C., Bennequin, D.: Affine invariance of human hand movements: a direct test. arXiv preprint arXiv:1209.1467 (2012)

    Google Scholar 

  91. Bergmann, J., Fei, J., Green, D., Howard, N.: Effect of Everyday Living Behavior on Cognitive Processing. PloS One (in preparation, 2013)

    Google Scholar 

  92. Bergmann, J., Langdon, P., Mayagoita, R., Howard, N.: Exploring the use of sensors to measure behavioral interactions: An experimental evaluation of using hand trajectories. PloS One (under review, 2013)

    Google Scholar 

  93. Deouell, L.Y., Heller, A.S., Malach, R., D’Esposito, M., Knight, R.T.: Cerebral responses to change in spatial location of unattended sounds. Neuron 55(6), 985–996 (2007)

    Google Scholar 

  94. Blank, H., Anwander, A., von Kriegstein, K.: Direct structural connections between voice-and face-recognition areas. The Journal of Neuroscience 31(36), 12906–12915 (2011)

    Google Scholar 

  95. Spiegler, A., Knösche, T.R., Schwab, K., Haueisen, J., Atay, F.M.: Modeling brain resonance phenomena using a neural mass model. PLoS Computational Biology 7(12), e1002298 (2011)

    Google Scholar 

  96. Andrews, D.L., Demidov, A.A.: Resonance energy transfer. Wiley (1999)

    Google Scholar 

  97. Kitajo, K., Nozaki, D., Ward, L.M., Yamamoto, Y.: Behavioral stochastic resonance within the human brain. Physical Review Letters 90(21), 218103 (2181)

    Google Scholar 

  98. Howard, N., Pollock, R., Prinold, J., Sinha, J., Newham, D., Bergmann, J.: Effect of impairment on upper limb performance in an ageing sample population. In: Stephanidis, C., Antona, M. (eds.) UAHCI 2013, Part II. LNCS, vol. 8010, pp. 78–87. Springer, Heidelberg (2013)

    Google Scholar 

  99. Kronland-Martinet, R., Morlet, J., Grossmann, A.: Analysis of sound patterns through wavelet transforms. International Journal of Pattern Recognition and Artificial Intelligence 1(02), 273–302 (1987)

    Google Scholar 

  100. Traunmüller, H., Eriksson, A.: The frequency range of the voice fundamental in the speech of male and female adults, Manuscript, Department of Linguistics, University of Stockholm (1994), http://www.ling.su.se/staff/hartmut/aktupub.htm (accessed May 8, 2004)

  101. Barnes, G.R., Benson, A.J., Prior, A.R.: Visual-vestibular interaction in the control of eye movement. Aviat. Space Environ. Med. 49(4), 557–564 (1978)

    Google Scholar 

  102. Lerner, Y., Epshtein, B., Ullman, S., Malach, R.: Class information predicts activation by object fragments in human object areas. J. Cogn. Neurosci. 20(7), 1189–1206 (2008)

    Google Scholar 

  103. Nir, Y., Hasson, U., Levy, I., Yeshurun, Y., Malach, R.: Widespread functional connectivity and fMRI fluctuations in human visual cortex in the absence of visual stimulation. Neuroimage 30(4), 1313–1324 (2006)

    Google Scholar 

  104. He, B.J., Snyder, A.Z., Zempel, J.M., Smyth, M.D., Raichle, M.E.: Electrophysiological correlates of the brain’s intrinsic large-scale functional architecture. Proceedings of the National Academy of Sciences 105(41), 16039–16044 (2008)

    Google Scholar 

  105. Manning, J.R., Jacobs, J., Fried, I., Kahana, M.J.: Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans. The Journal of Neuroscience 29(43), 13613–13620 (2009)

    Google Scholar 

  106. Nir, Y., Dinstein, I., Malach, R., Heeger, D.J.: BOLD and spiking activity. Nature Neuroscience 11(5), 523–524 (2008)

    Google Scholar 

  107. Nir, Y., Mukamel, R., Dinstein, I., Privman, E., Harel, M., Fisch, L., Gelbard-Sagiv, H., Kipervasser, S., Andelman, F., Neufeld, M.Y.: Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nature Neuroscience 11(9), 1100–1108 (2008)

    Google Scholar 

  108. Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., Malach, R.: Intersubject synchronization of cortical activity during natural vision. Science 303(5664), 1634–1640 (2004)

    Google Scholar 

  109. Dinstein, I., Pierce, K., Eyler, L., Solso, S., Malach, R., Behrmann, M., Courchesne, E.: Disrupted neural synchronization in toddlers with autism. Neuron 70(6), 1218–1225 (2011)

    Google Scholar 

  110. Ramot, M., Fisch, L., Davidesco, I., Harel, M., Kipervasser, S., Andelman, F., Neufeld, M.Y., Kramer, U., Fried, I., Malach, R.: Emergence of Sensory Patterns during Sleep Highlights Differential Dynamics of REM and Non-REM Sleep Stages. The Journal of Neuroscience 33(37), 14715–14728 (2013)

    Google Scholar 

  111. Baldassarre, A., Lewis, C.M., Committeri, G., Snyder, A.Z., Romani, G.L., Corbetta, M.: Individual variability in functional connectivity predicts performance of a perceptual task. Proceedings of the National Academy of Sciences 109(9), 3516–3521 (2012)

    Google Scholar 

  112. Zou, Q., Ross, T.J., Gu, H., Geng, X., Zuo, X.N., Hong, L.E., Gao, J.H., Stein, E.A., Zang, Y.F., Yang, Y.: Intrinsic resting‐state activity predicts working memory brain activation and behavioral performance. Human Brain Mapping (2012)

    Google Scholar 

  113. Wang, X., Han, Z., He, Y., Caramazza, A., Bi, Y.: Where color rests: Spontaneous brain activity of bilateral fusiform and lingual regions predicts object color knowledge performance. Neuroimage (2013)

    Google Scholar 

  114. Cole, M.W., Yarkoni, T., Repovš, G., Anticevic, A., Braver, T.S.: Global connectivity of prefrontal cortex predicts cognitive control and intelligence. The Journal of Neuroscience 32(26), 8988–8999 (2012)

    Google Scholar 

  115. van den Heuvel, M.P., Stam, C.J., Kahn, R.S., Pol, H.E.H.: Efficiency of functional brain networks and intellectual performance. The Journal of Neuroscience 29(23), 7619–7624 (2009)

    Google Scholar 

  116. Wang, X., Han, Z., He, Y., Liu, L., Bi, Y.: Resting-state functional connectivity patterns predict Chinese word reading competency. PloS One 7(9), e44848 (2012)

    Google Scholar 

  117. Koyama, M.S., Di Martino, A., Zuo, X.-N., Kelly, C., Mennes, M., Jutagir, D.R., Castellanos, F.X., Milham, M.P.: Resting-state functional connectivity indexes reading competence in children and adults. The Journal of Neuroscience 31(23), 8617–8624 (2011)

    Google Scholar 

  118. Riedl, V., Valet, M., Wöller, A., Sorg, C., Vogel, D., Sprenger, T., Boecker, H., Wohlschläger, A.M., Tölle, T.R.: Repeated pain induces adaptations of intrinsic brain activity to reflect past and predict future pain. Neuroimage (2011)

    Google Scholar 

  119. Wager, T.D., Atlas, L.Y., Leotti, L.A., Rilling, J.K.: Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. The Journal of Neuroscience 31(2), 439–452 (2011)

    Google Scholar 

  120. Martin, A., Barnes, K.A., Stevens, W.D.: Spontaneous neural activity predicts individual differences in performance. Proceedings of the National Academy of Sciences 109(9), 3201–3202 (2012)

    Google Scholar 

  121. Freyer, F., Becker, R., Dinse, H.R., Ritter, P.: State-dependent perceptual learning. The Journal of Neuroscience 33(7), 2900–2907 (2013)

    Google Scholar 

  122. Barttfeld, P., Wicker, B., McAleer, P., Belin, P., Cojan, Y., Graziano, M., Leiguarda, R., Sigman, M.: Distinct patterns of functional brain connectivity correlate with objective performance and subjective beliefs. Proceedings of the National Academy of Sciences 110(28), 11577–11582 (2013)

    Google Scholar 

  123. Ventura-Campos, N., Sanjuán, A., González, J., Palomar-García, M.-Á., Rodríguez-Pujadas, A., Sebastián-Gallés, N., Deco, G., Ávila, C.: Spontaneous Brain Activity Predicts Learning Ability of Foreign Sounds. The Journal of Neuroscience 33(22), 9295–9305 (2013)

    Google Scholar 

  124. Zhu, Q., Zhang, J., Luo, Y.L., Dilks, D.D., Liu, J.: Resting-state neural activity across face-selective cortical regions is behaviorally relevant. The Journal of Neuroscience 31(28), 10323–10330 (2011)

    Google Scholar 

  125. Coste, C.P., Sadaghiani, S., Friston, K.J., Kleinschmidt, A.: Ongoing brain activity fluctuations directly account for intertrial and indirectly for intersubject variability in Stroop task performance. Cerebral Cortex 21(11), 2612–2619 (2011)

    Google Scholar 

  126. Mennes, M., Kelly, C., Zuo, X.-N., Di Martino, A., Biswal, B.B., Castellanos, F.X., Milham, M.P.: Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage 50(4), 1690–1701 (2010)

    Google Scholar 

  127. Hampson, M., Driesen, N.R., Skudlarski, P., Gore, J.C., Constable, R.T.: Brain connectivity related to working memory performance. The Journal of Neuroscience 26(51), 13338–13343 (2006)

    Google Scholar 

  128. Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., Reiss, A.L., Greicius, M.D.: Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of Neuroscience 27(9), 2349–2356 (2007)

    Google Scholar 

  129. Tambini, A., Ketz, N., Davachi, L.: Enhanced brain correlations during rest are related to memory for recent experiences. Neuron 65(2), 280–290 (2010)

    Google Scholar 

  130. Adelstein, J.S., Shehzad, Z., Mennes, M., DeYoung, C.G., Zuo, X.-N., Kelly, C., Margulies, D.S., Bloomfield, A., Gray, J.R., Castellanos, F.X.: Personality is reflected in the brain’s intrinsic functional architecture. PloS One 6(11), e27633 (2011)

    Google Scholar 

  131. Stevens, W.D., Buckner, R.L., Schacter, D.L.: Correlated low-frequency BOLD fluctuations in the resting human brain are modulated by recent experience in category-preferential visual regions. Cerebral Cortex 20, 1997–2006 (2010)

    Google Scholar 

  132. Lewis, C.M., Baldassarre, A., Committeri, G., Romani, G.L., Corbetta, M.: Learning sculpts the spontaneous activity of the resting human brain. Proceedings of the National Academy of Sciences 106(41), 17558–17563 (2009)

    Google Scholar 

  133. Albert, N.B., Robertson, E.M., Miall, R.C.: The resting human brain and motor learning. Current Biology 19(12), 1023–1027 (2009)

    Google Scholar 

  134. Taubert, M., Lohmann, G., Margulies, D.S., Villringer, A., Ragert, P.: Long-term effects of motor training on resting-state networks and underlying brain structure. Neuroimage 57(4), 1492–1498 (2011)

    Google Scholar 

  135. Yoo, K., Sohn, W.S., Jeong, Y.: Tool-use practice induces changes in intrinsic functional connectivity of parietal areas. Frontiers in Human Neuroscience, 7 (2013)

    Google Scholar 

  136. Wang, Z., Liu, J., Zhong, N., Qin, Y., Zhou, H., Li, K.: Changes in the brain intrinsic organization in both on-task state and post-task resting state. Neuroimage 62(1), 394–407 (2012)

    Google Scholar 

  137. Hasson, U., Nusbaum, H.C., Small, S.L.: Task-dependent organization of brain regions active during rest. Proceedings of the National Academy of Sciences 106(26), 10841–10846 (2009)

    Google Scholar 

  138. Eryilmaz, H., Van De Ville, D., Schwartz, S., Vuilleumier, P.: Impact of transient emotions on functional connectivity during subsequent resting state: A wavelet correlation approach. Neuroimage 54(3), 2481–2491 (2011)

    Google Scholar 

  139. Huber, R., Esser, S.K., Ferrarelli, F., Massimini, M., Peterson, M.J., Tononi, G.: TMS-induced cortical potentiation during wakefulness locally increases slow wave activity during sleep. PloS One 2(3), e276 (2007)

    Google Scholar 

  140. Harmelech, T., Preminger, S., Wertman, E., Malach, R.: The Day-After Effect: Long Term, Hebbian-Like Restructuring of Resting-State fMRI Patterns Induced by a Single Epoch of Cortical Activation. The Journal of Neuroscience 33(22), 9488–9497 (2013)

    Google Scholar 

  141. Werheid, K., Zysset, S., Müller, A., Reuter, M., von Cramon, D.Y.: Rule learning in a serial reaction time task: an fMRI study on patients with early Parkinson’s disease. Cognitive Brain Research 16(2), 273–284 (2003)

    Google Scholar 

  142. Spulber, I., Georgiou, P., Eftekhar, A., Toumazou, C., Duffell, L., Bergmann, J., McGregor, A., Mehta, T., Hernandez, M., Burdett, A.: Frequency analysis of wireless accelerometer and EMG sensors data: Towards discrimination of normal and asymmetric walking pattern. In: Book Frequency Analysis of Wireless Accelerometer and EMG Sensors Data: Towards Discrimination of Normal and Asymmetric Walking Pattern, pp. 2645–2648. IEEE (2012)

    Google Scholar 

  143. Eckmiller, R., Hartmann, G., Hauska, G.: Parallel processing in neural systems and computers. Elsevier Science Inc. (1990) (1990)

    Google Scholar 

  144. Turner, B.M., Forstmann, B.U., Wagenmakers, E.-J., Brown, S.D.: A Bayesian framework for simultaneously modeling neural and behavioral data. NeuroImage 72, 193–206 (2013)

    Google Scholar 

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Authors and Affiliations

  1. Massachusetts Institute of Technology, USA

    Newton Howard

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  1. Newton Howard
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  1. Universidad Autónoma del Estado de Hidalgo, Ciudad Universitaria,, Carretera Pachuca–Tulancingo km 4.5, Hidalgo, Mexico

    Félix Castro

  2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan Dios Bátiz s/n, Col. Nueva Industrial Vallejo, 07738, Mexico City, Mexico

    Alexander Gelbukh

  3. Tecnológico de Monterrey, Campus Estado de México,, Carretera Lago de Guadalupe Km 3.5, Atizapán de Zaragoza,, CP 52926, Estado de México, Mexico

    Miguel González

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Howard, N. (2013). The Twin Hypotheses. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45114-0_35

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  • DOI: https://doi.org/10.1007/978-3-642-45114-0_35

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