A NeuroCognitive Approach to Decision Making for the Reconstruction of the Metabolic Insulin Profile of a Healthy Person

  • S. D. Teddy
  • F. Yap
  • C. Quek
  • E. M. -K. Lai
Part of the Intelligent Systems Reference Library book series (ISRL, volume 4)

Abstract

Human decision-making is defined as a cognitive process in which a preferred option or a course of action is chosen from among a set of alternatives, based on certain information or considerations. One important facet of decision-making is to facilitate an appropriate response to a dynamic and uncertain environment. Dynamic decision-making is inherently complex, and it is characterized by multiple, interdependent, and real-time decisions, which occur in an environment that may change independently as a function of a sequence of actions. In order to acquire a certain degree of proficiency in such a decision making process, the decision makers often have to be subjected to a lengthy practice. This subsequently implies that decision-making in a dynamic environment is based on experience, and further reinforces the notion of dynamic decision making as a cognitive skill that can be developed through practice. As with the acquisition of other cognitive skills, decision makers improve their decision-making skills through the accumulation, recognition and refinement of encountered decision episodes. Pivotal to the development of cognitive skills including dynamic decision-making are the abilities to acquire new knowledge (learning) and to retain such knowledge for future references (memory). The human procedural memory system is a facet of the brain’s computational fabric that exhibits the capacity for learning and memory, and constitutes a vast array of meticulously calibrated knowledge bases for coordinated behaviors and skills that are manifested in everyday life. This chapter describes the use of a brain inspired, cerebellar-based learning memory model named PSECMAC to functionally model the process of autonomous decision-making in a dynamic, complex and uncertain environment. The PSECMAC network is primarily modeled after the cerebellar learning mechanism in which repeated trainings induce a greater fidelity and precision in the knowledge acquired. PSECMAC employs an experience-driven adaptive quantization scheme to construct its computing structure by allocating more memory cells to significant regions of the input stimuli feature space. The validity of this neurocognitive approach to decision making is subsequently evaluated by employing the PSECMAC learning memory model to dynamically model the autonomous decision making process of insulin regulation in the physiological control of the human glucose metabolic process. The objective of the study is to approximate the metabolic insulin dynamics of a healthy subject in response to food intakes. In this case, the physiological regulation of insulin can be perceived as a biological example of a dynamic decision making process in which the human body dynamically determines the amount of insulin necessary to maintain bodily homeostasis in response to food disturbances. The preliminary experimental results are encouraging.

Keywords

autonomous decision making human cerebellum procedural memory PSECMAC diabetes insulin dynamics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wilson, R.A., Keils, F.C. (eds.): The MIT Encyclopedia of the Cognitive Sciences. MIT Press, Cambridge (2001)Google Scholar
  2. 2.
    March, J.: Primer on Decision Making: How Decisions Happen. Free Press, New York (1994)Google Scholar
  3. 3.
    Welch, D.A.: Decisions, Decisions: The Art of Effective Decision Making. Prometheus Books (2001)Google Scholar
  4. 4.
    Edwards, W.: Dynamic decision theory and probabilistic information processing. Human Factors 4, 59–73 (1962)Google Scholar
  5. 5.
    Brehmer, B.: Strategies in real-time, dynamic decision making. In: Hogarth, R.M. (ed.) Insights in decision making, pp. 262–279. University of Chicago Press, Chicago (1997)Google Scholar
  6. 6.
    Gonzalez, C.: The relationship between task workload and cognitive abilities in dynamic decision making. Human Factors 46(3), 449–460 (2004)CrossRefGoogle Scholar
  7. 7.
    Gonzalez, C., Wimisberg, J.: Situation awareness in dynamic decision-making: Effects of practice and working memory. Journal of Cognitive Engineering and Decision Making 1(1), 56–74 (2007)Google Scholar
  8. 8.
    Anderson, J.R. (ed.): Cognitive Skills and Their Acquisition. Lawrence Erlbaum Associates, Mahwah (1981)Google Scholar
  9. 9.
    Tomporowski, P.D.: The Psychology of Skill: A life-Span Approach. Praeger (2003)Google Scholar
  10. 10.
    VanLehn, K.: Cognitive skill acquisition. In: Spence, J., et al. (eds.) Annual Review of Psychology, vol. 47, pp. 513–539. Annual Reviews, Palo Alto (1996)Google Scholar
  11. 11.
    Fitts, P.M., Posner, M.I.: Human Performance. Brooks/Cole, Belmont (1967)Google Scholar
  12. 12.
    Proctor, R.W., Reeve, E.G., Weeks, D.J.: A triphasic approach to the acquisition of response-selection skill. In: Bower, G.H. (ed.) The Psychology of Learning: Advances in research and theory, pp. 207–240. Academic Press, London (1990)Google Scholar
  13. 13.
    Gilboa, I., Schemidler, D.: Case-based knowledge and induction. IEEE Trans. Systems, Man and Cybernetics – Part A: Systems and Humans 30(2), 85–95 (2000)CrossRefGoogle Scholar
  14. 14.
    Pew, R.W., Mavor, A.S.: Modeling human and organizational behavior. National City Press, Washington (1998)Google Scholar
  15. 15.
    Pomerol, J.-C., Adam, F.: Understanding Human Decision Making – A Fundamental Step Towards Effective Intelligent Decision Support. Intelligent Decision Making: An AI –Based Approach, 3–40 (2008)Google Scholar
  16. 16.
    Eichenbaum, H.: The Cognitive Neuroscience of Memory: An Introduction. Oxford University Press, Oxford (2002)CrossRefGoogle Scholar
  17. 17.
    Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of Neural Science, 4th edn. McGraw-Hill, Health Professions Division (2000)Google Scholar
  18. 18.
    Sweatt, J.D.: Mechanisms of Memory. Elsevier, Amsterdam (2003)Google Scholar
  19. 19.
    Squire, L.R., Knowlton, B., Musen, G.: The structure and organization of memory. Annual Review of Psychology 44, 453–495 (1993)CrossRefGoogle Scholar
  20. 20.
    Middleton, F.A., Strick, P.L.: The cerebellum: an overview. Trends in Cognitive Sciences 27(9), 305–306 (1998)CrossRefGoogle Scholar
  21. 21.
    Thach, W.T.: What is the role of the cerebellum in motor learning and cognition? Trends in Cognitive Sciences 2(9), 331–337 (1998)CrossRefGoogle Scholar
  22. 22.
    Middleton, F.A., Strick, P.L.: Cerebellar output: motor and cognitive channels. Trends in Cognitive Sciences 2(9), 348–354 (1998)CrossRefGoogle Scholar
  23. 23.
    Albus, J.S.: Brains, Behavior and Robotics. BYTE Books, McGraw-Hill (1981)Google Scholar
  24. 24.
    Thach, W.T.: On the specific role of the cerebellum in motor learning and cognition: Clues from pet activation and lesion studies in man. Behav. Brain Sci. 19(3), 411–431 (1996)MathSciNetGoogle Scholar
  25. 25.
    Desmond, J.E., Fiez, J.A.: Neuroimaging studies of the cerebellum: language, learning and memory. Trends Cog. Sci. 2(9), 355–362 (1998)CrossRefGoogle Scholar
  26. 26.
    Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Essentials of Neural Science and Behavior, 1st edn. McGraw-Hill, New York (1996)Google Scholar
  27. 27.
    Tyrrell, T., Willshaw, D.: Cerebellar cortex: Its simulation and the relevance of Marr’s theory. Philosophical Transactions: Biological Sciences 336(1277), 239–257 (1992)CrossRefGoogle Scholar
  28. 28.
    Doya, K.: What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? Neural Networks 12, 961–974 (1999)CrossRefGoogle Scholar
  29. 29.
    Albus, J.S.: Marr and Albus theories of the cerebellum two early models of associative memory. In: Proc. IEEE Compcon (1989)Google Scholar
  30. 30.
    Albus, J.S.: A theory of cerebellar function. Mathematical Biosciences 10(1), 25–61 (1971)CrossRefGoogle Scholar
  31. 31.
    Marr, D.: A theory of cerebellar cortex. J. Physiol. London 202, 437–470 (1969)Google Scholar
  32. 32.
    Ito, M.: The Cerebellum and Neural Control. Raven Press, New York (1984)Google Scholar
  33. 33.
    Houk, J.C., Buckingham, J.T., Barto, A.G.: Models of the cerebellum and motor learning. Behav. Brain Sci. 19(3), 368–383 (1996)Google Scholar
  34. 34.
    Ito, M.: Mechanisms of motor learning in the cerebellum. Brain Res. 886, 237–245 (2000)CrossRefGoogle Scholar
  35. 35.
    Schutter, E.D.: A new functional role for cerebellar long term depression. Progress in Brain Research 114, 529–542 (1997)CrossRefGoogle Scholar
  36. 36.
    Berthier, N.E., Moore, J.W.: Cerebellar Purkinje cell activity related to the classically conditioned nictitating membrane response. Exp. Brain Res. 63, 341–350 (1986)CrossRefGoogle Scholar
  37. 37.
    Ojakangas, C.L., Ebner, T.J.: Purkinje cell complex and simple spike changes during a voluntary arm movement learning task in the monkey. J. Neurophysiol. 68, 2222–2236 (1992)Google Scholar
  38. 38.
    Ojakangas, C.L., Ebner, T.J.: Purkinje cell complex spike activity during voluntary motor learning: relationship to kinematics. J. Neurophysiol. 72, 2617–2630 (1994)Google Scholar
  39. 39.
    Kleim, J.A., et al.: Synaptogenesis and FOS expression in the motor cortex of the adult rat aftr motor skill learning. J. Neurosci. 16(14), 4529–4535 (1996)Google Scholar
  40. 40.
    Kleim, J.A., et al.: Selective synaptic plasticity within the cerebellar cortex following complex motor skill learning. Neurobiol. Learn. Mem. 69, 274–289 (1998)CrossRefGoogle Scholar
  41. 41.
    Kleim, J.A., et al.: Structural stability within the lateral cerebellar nucleus of the rat following complex motor learning. Neurobiol. Learn. Mem. 69, 290–306 (1998)CrossRefGoogle Scholar
  42. 42.
    Federmeier, K.D., Kleim, J.A., Greenough, W.T.: Learning-induces multiple synapse formation in rat cerebellar cortex. Neuroscience Letters 332, 180–184 (2002)CrossRefGoogle Scholar
  43. 43.
    Albus, J. S.: A new approach to manipulator control: The Cerebellar Model Articulation Controller (CMAC). J. Dynamic Syst., Meas., Contr., Trans. ASME 220–227 (1975) Google Scholar
  44. 44.
    Albus, J.S.: Data storage in the Cerebellar Model Articulation Controller (CMAC). J. Dynamic Syst., Meas., Contr., Trans. ASME, 228–233 (1975)Google Scholar
  45. 45.
    Ang, K.K., Quek, C.: Stock trading using PSEC and RSPOP: A novel evolving rough set-based neuro-fuzzy approach. In: IEEE Congress on Evolutionary Computation, pp. 1032–1039 (2005)Google Scholar
  46. 46.
    Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer, New York (1989)Google Scholar
  47. 47.
    Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Intl. Conf. Knowledge Discovery and Data Mining (1996)Google Scholar
  48. 48.
    Orfanidis, S.J.: Introduction to Signal Processing. Prentice-Hall, Englewood Cliffs (1995)Google Scholar
  49. 49.
    Voogd, J., Glickstein, M.: The anatomy of the cerebellum. Trends Cog. Sci. 2(9), 307–313 (1998)CrossRefGoogle Scholar
  50. 50.
    Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall, New Jersey (1985)MATHGoogle Scholar
  51. 51.
    Teddy, S.D., Quek, C., Lai, E.M.-K.: PSECMAC: A Novel Self-Organizing Multiresolution Associative Memory Architecture. IEEE Trans. Neural Netw. 19(4), 689–712 (2008)CrossRefGoogle Scholar
  52. 52.
    Rubin, R.J., Altman, W.M., Mendelson, D.N.: Health care expenditures for people with diabetes mellitus. J. Clin. Endocrinol. Metab. 78, 809A–809F (1992)Google Scholar
  53. 53.
    Gæde, P., et al.: Effect of a Multifactorial Intervention on Mortality in Type 2 Diabetes. The New England Journal of Medicine 358, 580–591 (2003)CrossRefGoogle Scholar
  54. 54.
    The DCCT Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The New England Journal of Medicine 329, 977–986 (1993)Google Scholar
  55. 55.
    Holman, R.R., et al.: 10-year follow up of intensive glucose control in type 2 diabetes. The New England Journal of Medicine 359, 1577–1589 (2008)CrossRefGoogle Scholar
  56. 56.
    Cryer, P.E., Davis, S.N., Shamoon, H.: Hypoglycemia in diabetes. Diabetes Care 26, 1902–1912 (2003)CrossRefGoogle Scholar
  57. 57.
    De Blasio, B.F., et al.: Onset of type 1 diabetes — a dynamical instability. Diabetes 48, 1677–1685 (1999)CrossRefGoogle Scholar
  58. 58.
    Bergman, R.N., Phillips, L.S., Cobelli, C.: Physiologic evaluation of factors controlling glucose tolerance in man. J. Clin. Invest. 68, 1456–1467 (1981)CrossRefGoogle Scholar
  59. 59.
    World Health Organization. Diabetes: The cost of diabetes. Fact Sheet No. 236 (2002)Google Scholar
  60. 60.
    American Diabetes Association. Economic costs of diabetes in the U.S. in 2002. Diabetes Care 269(3), 917–932 (2003)Google Scholar
  61. 61.
    Ashcroft, F.M., Ashcroft, S.J.H.: Insulin: Molecular Biology to Pathology. Oxford University Press, New York (1992)Google Scholar
  62. 62.
    Tyagi, P.: Insulin delivery systems: Present trends and the future direction. Indian Journal of Pharmacy 34, 379–389 (2002)Google Scholar
  63. 63.
    Cobelli, C., Ruggeri, A.: Evaluation of portal/peripheral route and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes – a modeling study. IEEE Transaction on Biomedical Engineering 30(2), 93–103 (1983)CrossRefGoogle Scholar
  64. 64.
    Fletcher, L., et al.: Feasibility of an implanted, closed-loop, blood-glucose control device. Immunology 230 (2001)Google Scholar
  65. 65.
    Schetky, L.M., Jardine, P., Moussy, F.: A closed loop implantable artificial pancreas using thin film nitinol MEMS pumps. In: Proc. Intl. Conf. Shape Memory and Superelastic Technologies (2003)Google Scholar
  66. 66.
    Sorensen, J.T.: A Physiologic Model of Glucose Metabolism in Man and its Use to Design and Assess Improved Insulin Therapies for Diabetes. PhD thesis, Departement of Chemical Engineering, MIT (1985)Google Scholar
  67. 67.
    Ollerton, R.L.: Application of optimal control theory to diabetes mellitus. International Journal of Control 50, 2503–2522 (1989)MATHMathSciNetCrossRefGoogle Scholar
  68. 68.
    Hovorka, R.: Management of diabetes using adaptive control. International Journal on Adaptive Control and Signal Processing 19, 309–325 (2005)MATHMathSciNetCrossRefGoogle Scholar
  69. 69.
    Fisher, M.E.: A semiclosed-loop algorithm for the control of blood glucose levels in diabetics. IEEE Transaction on Biomedical Engineering 38(1), 57–61 (1991)CrossRefGoogle Scholar
  70. 70.
    Topp, B., et al.: A model of b-cell mass, insulin, and glucose kinetics: Pathways to diabetes. Journal of Theoretical Biology 206(4), 605–620 (2000)CrossRefGoogle Scholar
  71. 71.
    Steil, G.M., et al.: Repeatability of insulin sensitivity and glucose effectiveness from the minimal model – implications for study design. Diabetes 43, 1365–1371 (1994)CrossRefGoogle Scholar
  72. 72.
    Puckett, W.R., Lightfoot, E.N.: A model for multiple subcutaneous insulin injections developed from individual diabetic patient data. American Journal of Physiology 269, E1115–E1124 (1995)Google Scholar
  73. 73.
    Bremer, T., Gough, D.A.: Is blood glucose predictable from previous values? Diabetes 48(3), 445–451 (1999)CrossRefGoogle Scholar
  74. 74.
    Epple, A., Brinn, J.E.: The Comparative Physiology of the Pancreatic Inslets. Springer, Berlin (1987)Google Scholar
  75. 75.
    Doliba, N.M., Matschinsky, F.M.: The Metabolic Basis of Insulin Secretion. In: Sperling, M.A. (ed.) Type I Diabetes: Etiology and Treatment. Humana Press, Totowa (2003)Google Scholar
  76. 76.
    Matschinsky, F.M.: A lesson in metabolic regulation inspired by the glucokinase glucose sensor paradigm. Diabetes 45, 223–241 (1996)CrossRefGoogle Scholar
  77. 77.
    Schuit, F.C., et al.: Glucose sensing in pancreatic b-cells: A model for the study of other glucose-regulated cells in gut, pancreas, and hypothalamus. Diabetes 50, 1–11 (2001)CrossRefGoogle Scholar
  78. 78.
    Schwartz, M.W., Porte Jr., D.: Diabetes, obesity and the brain. Science 307, 375–379 (2005)CrossRefGoogle Scholar
  79. 79.
    Porte Jr., D., Baskin, D.G., Schwartz, M.W.: Insulin signaling in the central nervous system. Diabetes 54(5), 1264–1276 (2005)CrossRefGoogle Scholar
  80. 80.
    Gribble, F.M.: A higher power for insulin. Nature 434, 965–966 (2005)CrossRefGoogle Scholar
  81. 81.
    Pocai, A., et al.: Hypothalamic KATP channels control hepatic glucose production. Nature 434, 1026–1031 (2005)CrossRefGoogle Scholar
  82. 82.
    Obici, S., et al.: Hypothalamic insulin signaling is required for inhibition of glucose production. Nature Medicine 8(12), 1376–1382 (2002)CrossRefGoogle Scholar
  83. 83.
    Fisher, S.J., Kahn, C.R.: Insulin signaling is required for insulin’s direct and indirect action on hepatic glucose production. J. Clin. Invest. 111(4), 463–468 (2003)Google Scholar
  84. 84.
    Woods, S.C., et al.: Chronic intracerebroventricular infusion of insulin reduces food intake and body weight of baboons. Nature 282, 503–505 (1979)CrossRefGoogle Scholar
  85. 85.
    Woods, S.C., et al.: Signals that regulate food intake and energy homeostasis. Science 280, 1378–1383 (1998)CrossRefGoogle Scholar
  86. 86.
    Schwartz, M.W.: Central nervous system control of food intake. Nature 404, 661–671 (2000)Google Scholar
  87. 87.
    Illinois Institute of Technology. GlucoSim: A web-based educational simulation package for glucose-insulin levels in the human body, http://216.47.139.198/glucosim/gsimul.html
  88. 88.
    Health Promotion Board Singapore, http://www.hpb.gov.sg
  89. 89.
    Nanyang Technological University Singapore. Centre for Computational Intelligence, School of Computer Engineering, http://www.c2i.ntu.edu.sg
  90. 90.
    Tung, W.L., Quek, C.: GenSoFNN: A generic self-organizing fuzzy neural network. IEEE Transactions on Neural Networks 13(5), 1075–1086 (2002)CrossRefGoogle Scholar
  91. 91.
    Ang, K.K., Quek, C.: RSPOP: Rough set-based pseudo outer-product fuzzy rule identification algorithm. Neural Computation 17(1), 205–243 (2005)MATHCrossRefGoogle Scholar
  92. 92.
    Teddy, S.D., Lai, E.M.-K., Quek, C.: Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence. IEEE Trans. Neural Netw. 18(6), 1658–1682 (2007)CrossRefGoogle Scholar
  93. 93.
    Pasquier, M., Quek, C., Toh, M.: Fuzzylot: A self-organizing fuzzy-neural rule-based pilot system for automated vehicles. Neural Networks 14(8), 1099–1112 (2001)CrossRefGoogle Scholar
  94. 94.
    Zhou, R.W., Quek, C.: Antiforgery: A novel pseudo-outer product based fuzzy neural network driven signature verification system. Pat. Recog. Lett. 230(14), 1795–1816 (2002)Google Scholar
  95. 95.
    Ang, K.K., Quek, C., Wahab, A.: MCMAC-CVT: A novel on-line associative memory based CVT transmission control system. Neural Networks 15, 219–236 (2001)CrossRefGoogle Scholar
  96. 96.
    Tung, W.L., Quek, C., Cheng, P.Y.K.: GenSo-EWS: A novel neural-fuzzy based early warning system for predicting bank failures. Neural Networks 17, 567–587 (2004)CrossRefGoogle Scholar
  97. 97.
    Tung, W.L., Quek, C.: GenSo-FDSS: a neural-fuzzy decision support system for pediatric all cancer subtype identification using gene expression data. Artificial Intelligence in Medicine 336(1), 61–88 (2005)CrossRefGoogle Scholar
  98. 98.
    Tan, T.Z., Ng, G.S., Quek, C.: A novel biologically and psychologically inspired fuzzy decision support system: Hierarchical complementary learning. IEEE-ACM Trans. Computational Biology and BioInformatics 5(1), 67–79 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • S. D. Teddy
    • 1
  • F. Yap
    • 2
  • C. Quek
    • 3
  • E. M. -K. Lai
    • 4
  1. 1.Data Mining DepartmentInstitute for Infocomm Research, A*STARConnexis (South Tower)Singapore
  2. 2.KK Women’s and Children’s HospitalSingapore
  3. 3.Center for Computational Intelligence, Block N4 #2A-32, School of Computer EngineeringNanyang Technological UniversitySingapore
  4. 4.School of Engineering and Advanced TechnologyMassey UniversityWellingtonNew Zealand

Personalised recommendations