Journal of Computational Neuroscience

, Volume 16, Issue 2, pp 177–201 | Cite as

A Dynamical Model of Fast Cortical Reorganization

  • Marcelo Mazza
  • Marilene de Pinho
  • José Roberto C. Piqueira
  • Antônio C. Roque


In this work we study the connection between some dynamic effects at the synaptic level and fast reorganization of cortical sensory maps. By using a biologically plausible computational model of the primary somatosensory system we obtained simulation results that can be used to relate the dynamics of the interactions of excitatory and inhibitory neurons to the process of somatotopic map reorganization immediately after peripheral lesion. The model consists of three regions integrated into a single structure: tactile receptors representing the glabrous surface of the hand, ventral posterior lateral nucleus of the thalamus and area 3b of the primary somatosensory cortex, reproducing the main aspects of the connectivity of these regions. By applying informational measures to the simulation results of the dynamic behavior of AMPA, NMDA and GABA synaptic conductances we draw some conjectures about how the several neuronal synaptic elements are related to the initial stage of the digit-induced reorganization of the hand map in the somatosensory cortex.

cortical reorganization dynamic plasticity somatosensory system information theory 


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  1. Arbib MA, Érdi P, Szentágothai J (1998) Neural Organization: Structure, Function and Dynamics. MIT Press, Cambridge, MA.Google Scholar
  2. Armentrout SL, Reggia JA, Weinrich M (1994) A neural model of cortical map reorganization following a focal lesion. Artif. Intell. Med. 6: 383-400.Google Scholar
  3. Ascher P, Nowak L (1988) The role of divalent cations in the N-methyl-D-aspartate responses of mouse central neurons in culture. J. Physiol. Lond. 399: 247-266.Google Scholar
  4. Baldi P, Vanier MC, Bower JM (1998) On the use of Bayesian methods for evaluating compartmental neural models. J. Comp. Neurosci. 5: 285-314.Google Scholar
  5. Bear MF, Cooper LN, Ebner FF (1987) A physiological basis for a theory of synapse modification. Science 237: 42-48.Google Scholar
  6. Benusková L, Diamond ME, Ebner FF (1994) Dynamic synaptic modification threshold: A computational model of experience-dependent plasticity in adult rat barrel cortex. Proc. Natl Acad. Sci. USA 91: 4791-4795.Google Scholar
  7. Benusková L, Rema V, Armstrong-James M, Ebner, FF (2001) Theory for a normal and impaired experience-dependent plasticity in neocortex of adult rats. Proc. Natl Acad. Sci. USA 98: 2797-2802.Google Scholar
  8. Bienenstock EL, Cooper LN, Munro PW (1982) Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2: 32-48.Google Scholar
  9. Bloomfield SA, Hamos JE, Shernan SM (1987) Passive cable properties and morphological correlates of neurons in the lateral geniculate nucleus of the cat. J. Physiol. 383: 653-692.Google Scholar
  10. Bower JM (1992) Modeling the nervous system. Trends Neurosci. 15: 411-412.Google Scholar
  11. Bower JM, Beeman D (1998) The Book of GENESIS: Exploring Realistic Neural Models with General Neural Simulation System, 2nd edn. Telos, Santa Clara, CA.Google Scholar
  12. Buonomano DV, Merzenich MM (1998) Cortical plasticity: From synapses to maps. Annu. Rev. Neurosci. 21: 149-186.Google Scholar
  13. Calford MB, Tweedale R (1991) Immediate expansion of receptive fields of neurons in area 3b of macaque monkeys after digit denervation. Somatosens. Motor Res. 8: 249-260.Google Scholar
  14. Calford MB (2002) Dynamic representational plasticity in sensory cortex. Neurosci. 111: 709-738.Google Scholar
  15. Chen QX, Wong RKS (1995) Suppression of GABAA receptor responses by NMDA application in hippocampal neurons acutely isolated from the adult guinea-pig. J. Physiol. 482: 353-362.Google Scholar
  16. Clark SA, Allard T, Jenkins WM, Merzenich MM (1988) Receptive fields in the body-surface map in adult cortex defined by temporally correlated inputs. Nature 332: 444-445.Google Scholar
  17. Churchill JD, Muja N, Myers WA, Besheer J, Garraghty PE (1998) Somatotopic consolidation: A third phase of reorganization after peripheral nerve injury in adult squirrel monkeys. Exp. Brain Res. 118: 189-196.Google Scholar
  18. Connors BW, Gutnick MJ, Prince DA (1982) Electrophysiological properties of neocortical neurons in vitro. J. Neurophysiol. 48: 1302-1320.Google Scholar
  19. Cusick CG, Wall JT, Whiting JH, Wiley RG (1990) Temporal progression of cortical reorganization following nerve injury. Brain Res. 537: 355-358.Google Scholar
  20. Das A (1997) Plasticity in adult sensory cortex: A review. Network 8: R33-R76.Google Scholar
  21. Destexhe A, Mainen ZF, Sejnowski TJ (1998) Kinetic models of synaptic transmission. In: C Koch, I Segev, eds. Methods in Neural Modeling: From Synapses to Networks, 2nd, edn. MIT Press, Cambridge, MA, pp. 1-25.Google Scholar
  22. Durand D, Carlen P (1985) Electrotonic parameters of neurons following chronic ethanol consumption. J. Neurophysiol. 54: 807-817.Google Scholar
  23. Finnerty GT, Roberts LSE, Connors BW (1999) Sensory experience modifies the short-term dynamics of neocortical synapses. Nature 400: 367-371.Google Scholar
  24. Fox K (1995) The critical period for long-term potentiation in primary sensory cortex. Neuron 15: 485-488.Google Scholar
  25. Gardner EP, Palmer CI (1989) Simulation of motion skin. I. Receptive fields and temporal frequency coding by cutaneous mechanoreceptors of OPTA-CON pulses delivered to the hand. J. Neurophysiol. 62: 1410-1436.Google Scholar
  26. Garraghty PE, Kaas JH (1991) Functional reorganization in adult monkey thalamus after peripheral nerve injury. Neurorep. 2: 747-750.Google Scholar
  27. Garraghty PE, Kaas JH (1992) Dynamic features of sensory and motor maps. Current. Opin. Neurobiol. 2: 522-527.Google Scholar
  28. Garraghty PE, Muja N (1996) NMDA receptors and plasticity in adult primate somatosensory cortex. J. Comp. Neurol. 367: 319-326.Google Scholar
  29. Gibson JM, Beitel RE, Welker W (1975) Diversity of coding profiles of mechanoreceptors in glabrous skin kittens. Brain Res. 86: 181-203.Google Scholar
  30. Gilbert CD, Wiesel TN (1985) Intrinsic connectivity and receptive field properties in visual cortex. Vision Res. 25: 365-374.Google Scholar
  31. Goldreich D, Kyriazi H, Simons D (1999) Functional independence of layer IV barrels in rodent somatosensory cortex. J. Neurophysiol. 82: 1311-1316.Google Scholar
  32. Grajski KA, Merzenich MM (1990) Hebb-type dynamics is sufficient to account for the inverse magnification rule in cortical somatotopy. Neural. Comput. 2: 71-84.Google Scholar
  33. Grossberg S (2000) Linking mind to brain: The mathematics of biological intelligence. Not. Americ. Mathem. Soc. 47: 1361-1372.Google Scholar
  34. Hebb DO (1949) The Organization of Behaviour. Wiley, New York.Google Scholar
  35. Hille B (1984) Ionic Channels of Excitable Membranes. Sinauer, Sunderland, MA.Google Scholar
  36. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application of conduction and excitation in nerve. J. Physiol. Lond. 117: 500-544.Google Scholar
  37. Jahr C, Stevens C (1990) A quantitative description of NMDA receptor-channel kinetic behavior. J. Neurosci. 10: 1830-1837.Google Scholar
  38. Jenkins WM, Merzenich MM (1987) Reorganization of neocortical representations after brain injury: A neurophysiological model of the bases of recovery from stroke. Prog. Brain Res. 71: 249-266.Google Scholar
  39. Johansson RS (1978) Tactile sensibilty in the human hand: Receptive field characteristics of mechanoreceptive units in the glabrous skin area. J. Neurophysiol. (London) 281: 101-123.Google Scholar
  40. Jones EG (1985) The Thalamus. Plenum Press, New York.Google Scholar
  41. Jones EG (1998) Viewpoint: The core and the matrix of thalamic organization. Neurosci. 85: 331-345.Google Scholar
  42. Kaas JH, Nelson RJ, Sur M, Lin CS, Merzenich MM (1979) Multiple representations of the body within the primary somatosensory cortex of primates. Science 204: 521-523.Google Scholar
  43. Kaas JH (1991) The reorganization of sensory and motor maps after injury in adult mammals. Annu. Rev. Neurosci. 14: 137-167.Google Scholar
  44. Kaas JH (2000) Plasticity of sensory and motor maps in adult mammals. In Gazzaniga MS, The New Cognitive Neurosciences, 4th edition. MIT Press, Cambridge, pp. 223-236.Google Scholar
  45. Kandel ER, Schwartz JH, Jessel TM (2000) Principles of Neural Science. McGraw Hill, New York.Google Scholar
  46. Kawaguchi Y (1993) Groupings of nonpyramidal and pyramidal cells with specific physiological and morphological characteristics in rat frontal cortex. J. Neurophysiol. 69: 416-431.Google Scholar
  47. Khinchin AI (1957) Mathematical Foundation of Information Theory. Dover, New York.Google Scholar
  48. Koch C (1999) Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, New York.Google Scholar
  49. Kolarik RC, Rasey SK, Wall JT (1994) The consistency, extent, and location of early-onset changes in cortical nerve dominance aggregates following injury of nerves to primate hands. J. Neurosci. 14: 4269-4288.Google Scholar
  50. Larkman A, Mason A (1990) Correlations between morphology and electrophysiology of pyramidal neurons in slices of rat visual cortex. I. establishment of cell classes. J. Neurosci. 10: 1407-1414.Google Scholar
  51. Lenz FA, Kwan HCV, Martín R, Tasker R, Richardson RT, Dostrovsky JO (1994) Characteristics of somatotopic organization and spontaneous neuronal activity in the region of the thalamic principal sensory nucleus in patients with spinal cord transection. J. Neurophysiol. 72: 1570-1587.Google Scholar
  52. Lenz FA, Garonzik IM, Zihr TA, Dougherty PM (1998) Neuronal activity in the region of the thalamic principal sensory nucleus (ventralis caudalis) in patients with pain following amputations. Neurosci. 86: 1065-1081.Google Scholar
  53. Lytton WW, Sejnowiski TJ (1991) Simulations of cortical pyramidal neurons synchronized to inhibitory interneurons. J. Neurophysiol. 66: 1059-1079.Google Scholar
  54. Mazza MB, de Pinho M, Roque AC (1999) Biologically plausible models of topographic map formation in the somatosensory and auditory cortices. Int. J. Neural Syst. 9: 265-271.Google Scholar
  55. Mazza M, de Pinho M, Piqueira JRC, Roque AC (2002) Using information theory for the analysis of cortical reorganization in a realistic computational model of the somatosensory system. Neurocomputing 44-46: 923-928.Google Scholar
  56. McCormick DA, Connors BW, Lighthall JW (1985) Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. J. Neurophysiol. 54: 782-806.Google Scholar
  57. McCowan B, Hanser SF, Doyle LR (1999) Quantitative tools for comparing animal communication systems: Information theory applied to bottlenose dolphin whistle repertoires. Anim. Behav. 57: 409-419.Google Scholar
  58. Mel BW (1993) Synaptic integration in an excitable dendritic tree. J. Neurophysiol. 70: 1086-1101.Google Scholar
  59. Merzenich MM, Kaas JH, Wall JT, Nelson RJ, Sur M, Felleman DJ (1983a) Topographic reorganization of somatosensory cortical areas 3b and 1 in adult monkeys following restricted deafferentation. Neurosci. 8: 33-55.Google Scholar
  60. Merzenich MM, Kaas JH, Wall JT, Sur M, Nelson RJ, Sur M, Felleman DJ (1983b) Progression of change following median nerve section in the cortical representation of the hand in areas 3b and 1 in adult owl and squirrel monkeys. Neurosci. 10: 639-665.Google Scholar
  61. Merzenich MM, Nelson, Stryker MP, Cynader MS, Schopmann A, Zook JM (1984) Somatosensory map changes following digit amputation in adult monkeys. J. Comp. Neurology 244: 591-605.Google Scholar
  62. Merzenich MM, Nelson RJ, Kaas JH, Stryker MP, Jenkins WM (1987) Variability in hand surface representations in areas 3b and 1 in adult owl and squirrel monkeys. J. Comp. Neurology 258: 281-296.Google Scholar
  63. Merzenich MM, Sameshima K (1993) Cortical plasticity and memory. Curr. Opin. Neurobiol. 3: 187-196.Google Scholar
  64. Miller KD, Keller JB, Stryker MP (1989) Ocular dominance column development: Analysis and simulation. Science. 245: 605-615.Google Scholar
  65. Myers WA, Churchill JD, Muja N, Garraghty PE (2000) Role of NMDA receptors in adult primate cortical somatosensory plasticity. J. Comp. Neurol. 418: 373-382.Google Scholar
  66. Nolte J (1993) The Human Brain: An Introduction to its Functional Anatomy. Mosby-Year Book, Saint Louis, MO.Google Scholar
  67. Nowak L, Bregestovski P, Ascher P, Herbet A, Prochiantz A (1984) Magnesium gates glutamate-activated channels in mouse central neurones. Nature 307: 462-465.Google Scholar
  68. Oliveira RF, Roque AC (2002) A biologically plausible neural network model of the primate primary visual system. Neurocomput. 44-46: 957-963.Google Scholar
  69. Panzeri S, Treves A, Schultz S, Rolls ET (1999) On decoding the responses of a population of neurons from short time windows. Neural Comput. 11: 1553-1577.Google Scholar
  70. Pape HC, McCormick DA (1995) Electrophysiological and pharmacological properties of interneurons in the cat dorsal lateral nucleus. Neurosci. 68: 1105-1125.Google Scholar
  71. Pearson JC, Finkel LM, Edelman GM (1987) Plasticity in the organization of adult cerebral cortical maps: A computer simulation based on neuronal group selection. J. Neurosci. 7: 4209-4223.Google Scholar
  72. Piqueira JRC (1994) Structural and functional complexity: An informational approach. Proceedings of IEEE Conference on Systems, Man and Cybernetics, San Antonio-TX, pp. 1974-1978.Google Scholar
  73. Pongrácz F, Nicholas PP, Kocsis JD, Shepherd GM (1992) A model of NMDA receptor-mediated activity in dendrites of hippocampal CA1 pyramidal neurons. J. Neurophysiol. 68: 2248-2259.Google Scholar
  74. Rall W (1989) Cable theory for dendritic neurons. In: Koch C, Segev I, eds. Methods in Neural Modeling: From Synapses to Networks. MIT Press, Cambridge, MA, pp. 9-62.Google Scholar
  75. Rasmusson DD (1996) Changes in the response properties of neurons in the ventroposterior lateral thalamic nucleus of the racoon after peripheral deafferentation. J. Neurophysiol. 75: 2441-2450.Google Scholar
  76. Rieke F, Warland D, Steveninck RR, Bialek W (1997) Spikes: Exploring the Neural Code. MIT Press, Cambridge, MA.Google Scholar
  77. Roque da Silva AC (1992) Analysis of equilibrium properties of a continuous neural network made of excitatory and inhibitory neurons. Network 3: 303-321.Google Scholar
  78. Sanger TD, Merzenich MM (2000) Computational model of the role of sensory disorganization in focal task-specific dystonia. J. Neurophysiol. 84: 2458-2464.Google Scholar
  79. Segev I (1995) Dendritic processing. In: Arbib MA, ed. The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge, MA, pp. 282-289.Google Scholar
  80. Sherman RA, Koch C (1998) Thalamus. In RA Sherman, ed. The Synaptic Organization of the Brain. Oxford University Press, pp. 289-328.Google Scholar
  81. Shannnon C, Weaver W (1949) The Mathematical Theory of Communication. University of Illinois Press, Chicago.Google Scholar
  82. Shiryayev AN (1993) Selected Works of A.N. Kolmogorov. Kluwer Academic Publishers, Amsterdam.Google Scholar
  83. Silva MLD, Piqueira JRC, Vielliard JME (2000) Using Shannon Entropy on Measuring the Individual Variability in the Rufous-Bellied Trush Turdus Rufiventris Vocal Communication. J. Theor. Biol. 207: 57-64.Google Scholar
  84. Sirosh J, Miikkulainen R (1997) Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex. Neural. Comput. 9: 577-594.Google Scholar
  85. Sur M, Merzenich MM, Kaas JH (1980) Magnification, receptive-field area and hypercolumn size in owl monkeys. J. Neurophys. 44: 295-311.Google Scholar
  86. Takeuchi A, Amari S (1979) Formation of topographic maps and columnar microstructures in nerve fields. Biol. Cybern. 35: 63-72.Google Scholar
  87. Toma S, Nakajima Y (1995) Response characteristics of cutaneous mechanoreceptors to vibratory stimuli in human glabrous skin. Neurosci. Letters 195: 61-63.Google Scholar
  88. Traub RD, Miles R (1991) Model of the origin of rhythmic population oscillations in the hippocampal slice. Science 243: 1319-1325.Google Scholar
  89. Traub RD, Wong RK, Miles R, Michelson H (1991) A model of CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. J. Neurophysiol. 66: 635-650.Google Scholar
  90. von der Malsburg C (1973) Self-organization of orientation sensitive cells in the striata cortex. Kybernetik, 14: 84-100.Google Scholar
  91. Xing J, Gerstein GL (1996) Networks with lateral connectivity. III. plasticity and reorganization of somatosensory cortex. J. Neurophysiol. 75: 217-232.Google Scholar
  92. Xerri C, Merzenich MM, Petersen BE, Jenkins WM (1998) Plasticity of primary somatosensory cortex paralleling sensorimotor skill recovery from stroke in adult monkeys. J. Neurophysiol. 79: 2119-2148.Google Scholar
  93. Yuen GLF, Durand D (1991) Reconstruction of the hippocampal granule cell electrophysiology by computer simulation. Neurosci. 41: 411-425.Google Scholar
  94. Wall JT, Xu J, Wang X (2002) Human brain plasticity: An emerging view of the multiple substrates and mechanisms that cause cortical changes and related sensory dysfunction after injuries of sensoryinputs from the body. Brain Res. Rev. 39: 181-215.Google Scholar
  95. Wang X, Rinzel J, Ragawski MA (1991) A model of the t-type calcium current and the low threshold spike in thalamic neurons. J. Neurophysiol. 66: 839-850.Google Scholar
  96. Watanabe O (1992) Kolmogorov Complexity and Computational Complexity. Springer-Verlag, Berlin.Google Scholar
  97. White EL (1989) Cortical Circuits: Synaptic Organization of the Cerebral Cortex, Structure, Function and Theory. Birkhauser, Boston.Google Scholar
  98. Willshaw DJ, von der Malsburg C (1976) How patterned neural connections can be set up by self-organization. Proc. R. Soc. Lond. B 194: 431-445.Google Scholar
  99. Woolsey C, Marshall WH, Bard P (1942) Representation of cutaneous tactile sensibility in the cerebral cortex of the monkey as indicated by evoked potentials. Bull. Johns Hopkins Hosp. 70: 399-441.Google Scholar
  100. Zador A, Koch C, Brown TH (1990) Biophysical model of a Hebbian receptor channel kinetic behavior. Proc. Natl. Acad. Sci. USA 10: 97-133.Google Scholar

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© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Marcelo Mazza
    • 1
  • Marilene de Pinho
    • 1
  • José Roberto C. Piqueira
    • 1
  • Antônio C. Roque
    • 1
  1. 1.Departamento de Física e Matemática, FFCLRPUniversidade de São PauloAustralia

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