The Development of Cortical Models to Enable Neural-based Cognitive Architectures

  • Thomas McKenna

Abstract

The development of models of the cerebral cortex parallels the growth in sophistication of neural models in general, proceeding from heuristic models, to functional “black box” systems approaches, to large-scale neural circuit models. Currently, computational neuroscience is producing increasingly detailed neurobiologically based models of cortex and related structures. Many of these are intended as simulations of the biology, and are subject only to the constraint of predicting experimental observations in the neurobiological domain. A few of these models have some computational capability in the general domains of pattern recognition or control (Ambros-Ingerson et al., 1990; Carpenter and Grossberg, 1991; McKenna, 1994; Zornetzer et al., 1995).

Keywords

Mobile Robot Cognitive Skill Auditory Cortex Production Rule Neural Model 
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References

  1. Abeles, M. (1991) Corticonics. Cambridge, England: Cambridge University Press.CrossRefGoogle Scholar
  2. Abeles, M., Vaadia, E., Bergman, H., Prut, Y., Haalman, I., Slovin, H. (1993) Dynamics of neuronal interactions in the frontal cortex of behaving monkeys. Concepts in Neuroscience 4: 131–158.Google Scholar
  3. Aleksandrovsky, B., Whitson, J., Andes, G., Lynch, G., Granger, R. (1996a) Novel speech processing mechanism derived from auditory neocortical circuit analysis. Proc. ICSLP Int’l. Conf. On Spoken Language Proc. IEEE Press 1: 558–561.Google Scholar
  4. Aleksandrovsky, B., Whitson, J., Garzotto, A., Lynch, G., Granger, R. (1996b) An algorithm derived from thalamocortical circuitry stores and retrieves temporal sequences. Proc. Int’l. Conf. Pattern Recog., IEEE Comp. Soc. Press 4: 550–554.Google Scholar
  5. Aleksandrovsky, B., Brucher, F., Lynch, G. (1997a) Neural network model of striatal complex. In: Biological and Artificial Computation: From Neuroscience to Technology. IWANN’97 International Conference on Artificial and Natural Neural Networks, Lecture Notes in Computer Science 1240. Berlin: Springer-Verlag, pp. 103–115.CrossRefGoogle Scholar
  6. Aleksandrovsky, B., Whitson, J., Garzotto, A., Lynch, G., Granger, R. (1997b) A continuous temporal sequence recognition device based on a model of structure and function in the neocortex. Technical Report, Brain Theory Project, University of California, Irvine.Google Scholar
  7. Alexander, G.E., Crutcher, M.D. (1990) Functional architecture of basal ganglia circuits: Neural substrates of parallel processing. Trends in Neurosciences 13: 266–271.CrossRefGoogle Scholar
  8. Ambros-Ingerson, J., Granger, R., Lynch, G. (1990) Simulation of paleocortex performs hierarchical clustering. Science 247: 1344–1348.CrossRefGoogle Scholar
  9. Anderson, J.R. (1993) Rules of the Mind. Hillsdale, NJ: Erlbaum.Google Scholar
  10. Anderson, J.R., Matessa, M., Labiere, C. (1997) ACT-R: A theory of higher level cognition and its relation to visual attention. Human Computer Interaction 12: 439–462.CrossRefGoogle Scholar
  11. Angeline, P., Saunders, G., Pollack, J. (1994) An evolutionary algorithm that constructs recurrent networks. IEEE Trans. on Neural Networks 5: 54–65.CrossRefGoogle Scholar
  12. Anton, P., Lynch, G., Granger, R. (1991) Computation of frequency-to-spatial transform by olfactory bulb glomeruli. Biol. Cybern. 65: 407–414.CrossRefGoogle Scholar
  13. Bachmann, C.M., Musman, S., Luong, D., Schultz, A. (1994) Unsupervised BCM projection pursuit algorithms for classification of simulated radar presentations. Neural Networks 7: 709–728.CrossRefGoogle Scholar
  14. Bowers, J.M., Beeman, D. (Eds.) (1998) The Book of Genesis: Exploring Realistic Neural Models with the General Neural Simulation System. 2nd ed. New York: Springer-Verlag.Google Scholar
  15. Braitenberg, V., Schuz, A. (1991) Anatomy of the Cortex: Statistics and Geometry. Berlin: Springer-Verlag.Google Scholar
  16. Braver, T.S., Cohen, J.D. (1999) On the control of control: The role of dopamine in regulating prefrontal function and working memory. In: S. Monsell, J. Driver (Eds.) Attention and Performance XVII. Cambridge, MA: MIT Press.Google Scholar
  17. Braver, T.S., Cohen, J.D. (2001) Working memory, cognitive control, and the prefrontal cortex: computational and empirical studies. Cognitive Processing 2: 25–55.Google Scholar
  18. Brooks, R., Breazeal, C., Marjanović, M., Scassellati, B., Williamson, M. (1999) The Cog project: Building a humanoid robot. In: C. Nehaniv (Ed.) Computation for Metaphors, Analogy, and Agents, Lecture Notes in Artificial Intelligence 1562. New York: Springer-Verlag, pp. 52–87.CrossRefGoogle Scholar
  19. Bullock, D., Cisek, P.E., Grossberg, S. (1998) Cortical networks for control of voluntary arm movements under variable force conditions. Cerebral Cortex 8: 48–62.CrossRefGoogle Scholar
  20. Bullock, D., Fiala, J.C., Grossberg, S. (1994) A neural model of timed response learning in the cerebellum. Neural Networks 7: 1101–1114.MATHCrossRefGoogle Scholar
  21. Carpenter, G.A., Grossberg, S. (Eds.) (1991) Pattern Recognition by Self-Organizing Neural Networks. Cambridge, MA: The MIT Press.Google Scholar
  22. Carpenter, P.A., Just, M.A., Shell, P. (1990) What one intelligence test measures: A theoretical account of the Raven Progressive Matrices Test. Psychological Review 97: 404–431.CrossRefGoogle Scholar
  23. Chandrasekaran, A. (1994) Architecture of Intelligence: The problems and current approaches to solutions. In: V. Honavar, L. Uhr (Eds.) Artificial Intelligence and Neural Networks. New York: Academic Press.Google Scholar
  24. Chipman, S.F. (1992) The higher-order cognitive skills: What they are and how they might be transmitted. In: T.G. Sticht, B.A. McDonald, M.J. Beeler (Eds.) Intergenerational Transfer of Cognitive Skills: Vol. II: Theory and Research in Cognitive Science. Norwood, NJ: Ablex, pp. 128–158.Google Scholar
  25. Cohen, J.D., Braver, T.S., O’Reilly, R.C. (1996) A computational approach to prefrontal cortex, cognitive control and schizophrenia: recent developments and current challenges. Phil. Trans. R. Soc. Lond. B. 351: 1515–1527.CrossRefGoogle Scholar
  26. Cohen, J.D., Dunbar, K., McClelland, J.L. (1990) On the control of automatic processes: A parallel distributed processing model of the stroop effect. Psychological Review 97: 332–361.CrossRefGoogle Scholar
  27. Contreras-Vidal, J.L., Grossberg, S., Bullock, D. (1997) A neural model of cerebellar learning for arm movement control: Cortico-spinal-cerebellar dynamics. Learning and Memory 3: 475–502.CrossRefGoogle Scholar
  28. Cooper, R., Shallice, T. (1995) Soar and the case for unified theories of cognition.Cognition 55: 115–149.CrossRefGoogle Scholar
  29. Coultrip, R., Granger, R., Lynch, G. (1992) A cortical model of winner-take-all competition via lateral inhibition. Neural Networks 5: 47–54.CrossRefGoogle Scholar
  30. Cox, A.L. and Young, R.M. (2000) Device-oriented and task oriented exploratory learning of interface designs. In: Proceedings of the Third International Conference on Cognitive Modeling. Veenendaal, Netherlands: Universal Press, pp. 70–77.Google Scholar
  31. Deadwyler, S.A., Hampson, R.E. (1997) The significance of neural ensemble codes during behavior and cognition. Ann. Rev. Neurosci. 20: 217–244.CrossRefGoogle Scholar
  32. DeFelipe, J., Jones, E.G. (1988) Cajal on the Cerebral Cortex: An annotated translation of the complete writings. New York: Oxford University Press.Google Scholar
  33. Dehaene, S., Changeux, J.P. (1992) The Wisconsin card sorting test: Theoretical analysis and modeling in a neuronal network. Cerebral Cortex 1: 62–79.CrossRefGoogle Scholar
  34. Douglas, R.J., Koch, C., Mahowald, M., Martin, K., Suarez, H. (1995) Recurrent excitation in neocortical circuits. Science 269: 981–985.CrossRefGoogle Scholar
  35. Douglas, R.J., Martin, K.A.C. (1990) Neocortex. In: G.M. Sheperd (Ed.) The Synaptic Organization of the Brain, 3rd Ed. New York: Oxford University Press, pp. 389–438.Google Scholar
  36. Favorov, O.V., Hester, J.T., Kelly, D.G, Tommerdahl, M., Whitsel, B.L. (1998) Lateral interactions in cortical networks. In: M.J. Rowe (Ed.) Somatosensory Processing: From Single Neuron to Brain Imaging. Langhorne, PA: Harwood, pp.187–207.Google Scholar
  37. Feldman, J.A. (1991) Cognition as search. Science 251: 575.CrossRefGoogle Scholar
  38. Ferrell, C. (1996) Orientation behavior using registered topographic maps. In: From Animals to Animats, Proc. 1996 meeting of Soc. of Adaptive Behavior. Cape Cod, MA, pp. 94–103.Google Scholar
  39. Frank, M., Loughry, B., O’Reilly, R.C. (2001) Interactions between the frontal cortex and basal ganglia in working memory: A computational model. Cognitive, Affective, and Behavioral Neuroscience 1: 137–160.CrossRefGoogle Scholar
  40. Freeman, W.J. (1992) Tutorial in neurobiology: From single neurons to brain chaos. Int. J. Bifurcation and Chaos 2: 451–482.MATHCrossRefGoogle Scholar
  41. Gancarz, G., Grossberg, S. (1999) A neural model of saccadic eye movement control explains task-specific adaptation. Vision Res. 39: 3123–3143.CrossRefGoogle Scholar
  42. Garzotto, A., Aleksandrovsky, B., Lynch, G., Granger, R. (1997) A neocortically derived model of continuous contextual processing. Proc. International Conference on Neural Networks, IEEE Press 1: 564–568.Google Scholar
  43. Gluck, K.A. (2000) An ACT-R/PM model of algebra symbolization. In: N. Taatgen, J. Aasman (Eds.) Proceedings of the Third International Conference on Cognitive Modeling. Veenendaal, Netherlands: Universal Press, pp. 134–141.Google Scholar
  44. Gluck, M.A., Granger, R. (1993) Computational aspects of the neural bases of learning and memory. Annual Review of Neuroscience 16: 667–706.CrossRefGoogle Scholar
  45. Gluck, M.A., Myers, C.E. (1997) Psychological models of hippocampal function in learning and memory. Annual Review of Psychology 48: 481–514.CrossRefGoogle Scholar
  46. Granger, R., Ambros-Ingerson, J., Lynch, G. (1989) Derivation of encoding characteristics of layer II cerebral cortex. J. Cognit. Neurosci. 1: 61–87.CrossRefGoogle Scholar
  47. Granger, R., Cobas, A., Lynch, G. (1991a) Possible computations of primary sensory cortex: Hypotheses based on computer models of olfaction and audition. In: M. Baudry, J. Davis (Eds.) Current Issues in LTP. Cambridge, MA: MIT Press.Google Scholar
  48. Granger, R., Staubil, U., Powers, H., Otto, T., Ambros, J., Lynch, G. (1991b) Behavioral tests of a prediction from a cortical network simulation. Psychological Science 2: 116–118.CrossRefGoogle Scholar
  49. Granger, R., Wiebe, S.P., Taketani, Lynch, G. (1996) Distinct memory circuits composing the hippocampal region. Hippocampus 6: 567–578.CrossRefGoogle Scholar
  50. Grossberg, S. (1997) Cortical dynamics of three-dimensional figure-ground perception of two-dimensional pictures. Psychol. Rev. 104: 618–658.CrossRefGoogle Scholar
  51. Grossberg, S., Mingolla, E., Ross, W.D. (1997) Visual brain and visual perception: how does the cortex do perceptual grouping? Trends in Neurosciences 2: 106–111.CrossRefGoogle Scholar
  52. Gunzelmann, G., Anderson, J.R. (2001) Modeling the emergence of strategies and their effects on problem difficulty in ACT-R. In: Proceedings of the Fourth International Conference on Cognitive Modeling. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 109–114.Google Scholar
  53. Gupta, M.M., Knopf, G.K. (1994) Neuro-Vision Systems: Principles and Applications. New York: IEEE Press.MATHGoogle Scholar
  54. Hecht-Nielsen, R. (1998) A theory of the cerebral cortex. Proc. 1998 Int’l. Conf. on Neural Information Processing. Kitakyushu, Japan, pp. 1459–1464.Google Scholar
  55. Iatrou, M., Berger, T.W., Marmarelis, V.Z. (1999) Application of novel modeling method to the nonstationary properties of potentiation in the rabbit hippocampus. Annals of Biomedical Engineering 27: 581–591.CrossRefGoogle Scholar
  56. Just, M.A., Carpenter, P.A. (1985) Cognitive coordinate systems: Accounts of mental rotation and individual differences in spatial ability. Psychological Review 92: 137–172.CrossRefGoogle Scholar
  57. Just, M.A., Carpenter, P.A. (1992) A capacity theory of comprehension: Individual differences in working memory. Psychological Review 99: 122–149.CrossRefGoogle Scholar
  58. Just, M.A., Carpenter, P.A., Hemphill, D.D. (1996) Constraints on processing capacity: Architectural or implementational? In: D. Steier, T. Mitchell (Eds.) Mind Matters: A Tribute to Allan Newell. Mahwah, NJ: Erlbaum.Google Scholar
  59. Just, M.A., Carpenter, P.A., Shell, P. (1990) What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review 97: 404–431.CrossRefGoogle Scholar
  60. Just, M.A., Carpenter, P.A., Varma, S. (1999) Computational modeling of high-level cognition and brain function. Human Brain Mapping 8: 128–136.CrossRefGoogle Scholar
  61. Kilborn, K., Kubota, D., Lynch, G., Granger, R. (1998) Parameters of LTP induction modulate network categorization behavior. In: J.M. Bowers (Ed.) Computational Neuroscience: Trends in Research. New York: Plenum Press, pp. 353–358.Google Scholar
  62. Koch, C. (1999) Biophysics of Computation: Information Processing in Single Neurons. New York: Oxford University Press.Google Scholar
  63. Labiere, C. (1998) The dynamics of cognition: An ACT-R model of cognitive arithmetic. PhD Dissertation. CMU Computer Science Dept. Technical Report CMU-CS-98-186. Pittsburgh, PA. (http://reports-archive.adm.cs.cmu.edu/csl998.html)Google Scholar
  64. Laird, J.E., Newell, A., Rosenbloom, P.S. (1987) SOAR: An architecture for general intelligence. Artificial Intelligence 33(1): 1–63.MathSciNetCrossRefGoogle Scholar
  65. Landy, M.S., Movshon, J.A. (1991) Computational Models of Visual Processing. Cambridge, MA: MIT Press.Google Scholar
  66. Lund, J.S., Yoshioka, T., Levitt, J.B. (1994) Substrates for interlaminar connections in area V1 of macaque monkey cerebral cortex. In: A.A. Peters, K.S. Rockland (Eds.) Cerebral Cortex v. 10. New York: Plenum Press.Google Scholar
  67. Lyon, R., Shamma, S. (1996) Auditory representations of timbre and pitch. In: H.L. Hawkins, T.A. McMullen, A.N. Popper, R.R. Fay (Eds.) Auditory Computation. Berlin: Springer-Verlag, pp. 221–270.CrossRefGoogle Scholar
  68. Martin, K.A.C. (1988) The Wellcome Prize lecture: from single cells to simple circuits in the cerebral cortex. Quart. J. Exp. Physiol. 73: 637–702.Google Scholar
  69. McKenna, T.M. (1994) The role of interdisciplinary research involving neuroscience in the development of intelligent systems. In: V. Honavar, L. Uhr (Eds.) Artificial Intelligence and Neural Networks. New York: Academic Press.Google Scholar
  70. McKenna, T., Davis, J., Zornetzer, S.F. (1992) Single Neuron Computation. Boston: Academic Press.MATHGoogle Scholar
  71. McKenna, T.M., McMullen, T.A., Shlesinger, M.F. (1994) The brain as a dynamic physical system. Neuroscience 60: 587–605.CrossRefGoogle Scholar
  72. Meyer, D.E., Kieras, D.E. (1997) A computational theory of executive cognitive processes and human multiple-task performance: Part 1. Basic mechanisms. Psychological Review 104: 3–65.CrossRefGoogle Scholar
  73. Mitani, A., Shimokouchi, M., Itoh, K., Nomura, S., Kudo, M. Mizuno, N. (1985) Morphology and laminar organization of electrophysiologically identified neurons in the primary auditory cortex. J. Comp. Neurol. 235: 430–447.CrossRefGoogle Scholar
  74. Newell, A. (1992) Unified theories of cognition and the role of Soar. In: J.A. Michon, A. Akyürek (Eds.) SOAR: A Cognitive Architecture in Perspective. Dordrecht, The Netherlands: Kluwer Academic Publishers, pp. 25–79.CrossRefGoogle Scholar
  75. Nicolelis, M.A.L., Baccala, L.A., Lin, R.C.S., Chapin, J.K. (1995) Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system. Science 268: 1353–1358.CrossRefGoogle Scholar
  76. Nicolelis, M.A.L., Fanselow, E.E., Ghazanfar, A.A. (1997) Hebb’s dream: The resurgence of cell assemblies. Neuron 19: 219–221.CrossRefGoogle Scholar
  77. Nicolelis, M.A.L., Fanselow, E.E., Shuler, M. Henriquez, C. (2001) A critique of the pure feedforward model of touch. In: R.J. Nelson (Ed.) The Somatosensory System: Deciphering the Brain’s Own Body System. Boca Raton, FL: CRC Press.Google Scholar
  78. Nicolelis, M.A.L., Katz, D., Krupa, D.J. (1998) Potential circuit mechanisms underlying concurrent thalamic and cortical plasticity. Rev. Neurosci. 9: 213–224.CrossRefGoogle Scholar
  79. O’Reilly, R.C. (1996) Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm. Neural Comput. 8: 895–938.CrossRefGoogle Scholar
  80. O’Reilly, R.C. (1998) Six principles for biologically based computational models of cortical cognition. Trends in Cognitive Sciences 2: 455–462.CrossRefGoogle Scholar
  81. O’Reilly, R.C. (2001) Generalization in interactive networks: The benefits of inhibitory competition and Hebbian learning. Neural Computation 13: 1199–1241.MATHCrossRefGoogle Scholar
  82. O’Reilly, R.C., Braver, T.S., Cohen, J.D. (1999). A biologically-based computational model of working memory. In: A. Miyake, P. Shah (Eds.) Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York: Cambridge University Press, pp. 375–411.Google Scholar
  83. O’Reilly, R.C., Munakata, Y. (2000) Computational Explorations in Cognitive Neuro-science: Understanding of the Mind by Simulating the Brain. Cambridge, MA: MIT Press.Google Scholar
  84. O’Reilly, R.C., Noelle, D.C., Braver, T.S., Cohen, J.D. (2002) Prefrontal cortex in dynamic categorization tasks: Representational organization and neuromodulatory control. Cerebral Cortex 12: 246–257.CrossRefGoogle Scholar
  85. O’Reilly, R.C., Rudy, J.W. (2001) Conjunctive representations in learning and memory: Principles of cortical and hippocampal function. Psychological Review 108: 311–345.CrossRefGoogle Scholar
  86. Pearce, T.C, Vershure, P.F.M.J., White, J., Kauer, J.S. (2001) Stimulus encoding during the early stages of olfactory processing: A modeling study using an artificial olfactory system. Neurocomputing 38: 299–306.CrossRefGoogle Scholar
  87. Port, R.F., van Gelder, T. (1995) Mind as Motion: Explorations in the Dynamics of Cognition. Cambridge, MA: MIT Press.Google Scholar
  88. Rosenbloom, P.S., Laird, J.E., Newell, A. (1993) The Soar Papers: Readings on Integrated Intelligence. Cambridge, MA: MIT Press.Google Scholar
  89. Salvucci, D.D., Boer, E.R., Liu, A. (2002) Toward an integrated model of driver behavior in a cognitive architecture. Transportation Research Record (in press).Google Scholar
  90. Scassellati, B. (1998) A binocular, foveated active vision system. Technical Report, Memo 1628, MIT Artificial Intelligence Lab.Google Scholar
  91. Schoenbaum, G., Eichenbaum, H. (1995) Information coding in the rodent prefrontal cortex. II. Ensemble activity in the orbitofrontal cortex. J. Neurophysiol. 70: 28–36.Google Scholar
  92. Schunn, C., Anderson, J.R. (1998) Scientific discovery. In: J.R. Anderson, C. Labiere (Eds.) The Atomic Components of Thought. Mahwah, NJ: Erlbaum, pp. 255–296.Google Scholar
  93. Schunn, C., Harrison, A. (2001) ACT-RS: A neuropsychologically inspired model for spatial reasoning. In: Proceedings of the Fourth International Conference on Cognitive Modeling. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 267–268.Google Scholar
  94. Schwartz, E.L., Greve, D.N., Bonmassar, G. (1995) Space-variant active vision: Definition, overview and examples. Neural Networks 8: 1297–1308.CrossRefGoogle Scholar
  95. Segev, I., Rinzel, J., Sheperd, G. (1995) The Theoretical Foundation of Dendritic Function: Selected Papers of Wilfred Rall with Commentaries. Cambridge, MA: MIT Press.Google Scholar
  96. Shamma, S. (1997) Auditory cortical representation of complex acoustic spectra as inferred from the ripple analysis method. Network: Computation in Neural Systems 7: 439–476.CrossRefGoogle Scholar
  97. Shastri, L., Ajjanagadde, V. (1993) From simple associations to systematic reasoning: a connectionist representation of rules, variable, and dynamic bindings using temporal synchrony. Behav. Brain Sci. 16: 417–494.CrossRefGoogle Scholar
  98. Singer, W. (1994) Putative functions of temporal correlations in neocortical processing. In: C. Koch, J. Davis (Eds.) Large-Scale Neuronal Theories of the Brain. Cambridge, MA: MIT Press, pp. 201–237.Google Scholar
  99. Snow, R.E., Kyllonen, P.C., Marshalek, B. (1984) The topography of ability and learning correlations. In: R.J. Sternberg (Ed.) Advances in the Psychology of Human Intelligence, Vol. 2. Hillsdale, NJ: Erlbaum, pp. 47–103.Google Scholar
  100. Sohn, M.-H., Ursu, S., Anderson, J.R., Stenger, V.A., Carter, C.S. (2000) The role of prefrontal cortex and posterior parietal cortex in task-switching. Proceedings of National Academy of Science, 13448-13453.Google Scholar
  101. Thelen, E., Smith, L.B. (1994) A Dynamic Systems Approach to the Development of Cognition and Action. Cambridge, MA: MIT Press.Google Scholar
  102. Thibadeau, R., Just, M.A., Carpenter, P.A. (1982) A model of the time course and content of reading. Cognitive Science 6: 157–203.CrossRefGoogle Scholar
  103. Thomson, A.M., Deuchars, J. (1994) Temporal and spatial properties of local circuits inneocortex. Trends in Neurosciences 17: 119–126.CrossRefGoogle Scholar
  104. Vincente, K.J., Kirklik, A. (1992) On putting the cart before the horse: Taking perception seriously in unified theories of cognition. Behavioral and Brain Sciences 15: 461–162.CrossRefGoogle Scholar
  105. Wang, K., Shamma, S. (1995) Representation of acoustic signals in the primary auditory cortex. IEEE Trans. Audio and Speech Proc. V3(5): 382–395.CrossRefGoogle Scholar
  106. Weinberger, N.M. (1995) Dynamic regulation of receptive fields and maps in the adult sensory cortex. Ann. Rev. Neurosci. 18: 129–158.CrossRefGoogle Scholar
  107. Weinberger, N.M., Ashe, J.H., Metherate, R., McKenna, T.M., Diamond, D.M., Bakin, J.S., Lennartz, R.C., Cassady, J.M. (1990) Neural adaptive information processing: A preliminary model of receptive-field plasticity in auditory cortex during Pavlovian conditioning. In: M. Gabriel, J. Moore (Eds.) Learning and Computational Neuroscience: Foundations of Adaptive Networks. Cambridge, MA: MIT Press, pp. 91–138.Google Scholar
  108. White, J., Dickinson, T.A., Walt, D.R., Kauer, J.S. (1998) An olfactory neuronal network for vapor recognition in an artificial nose. Biological Cybernetics 78: 245–251.MATHCrossRefGoogle Scholar
  109. White, J., Kauer, J.S. (1999) Odor recognition in an artificial nose by spatio-temporal processing using an olfactory neuronal network. Neurocomputing 26: 919–924.CrossRefGoogle Scholar
  110. Wickens, J. (1997) Basal ganglia: Structure and computations. Network: Computation in Neural Systems 8: 77–109.CrossRefGoogle Scholar
  111. Wills, H.R., Kellogg, M.M., Goodman, P.H. (1999) A biologically realistic computer of neocortical associative learning for the study of aging and dementia. J. Invest. Med.47(2): 11AGoogle Scholar
  112. Zachary, W., Ryder, J., Hicinbothom, J. (1998) Cognitive task analysis and modeling of decision making in complex environments. In: J. Cannon-Bowers, E. Salas (Eds.) Decision Making Under Stress: Implications for Training and Simulation. Washington, DC: American Psychological Association Press.Google Scholar
  113. Zornetzer, S., Davis, J., Lau, C., McKenna, T. (Eds.) (1995) An Introduction to Neural and Electronic Networks (2nd Ed.). San Diego: Academic Press.Google Scholar

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  • Thomas McKenna

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