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Effiects of different connectivity patterns in a model of cortical circuits

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Computational Methods in Neural Modeling (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2686))

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Abstract

Cortical circuits are usually modeled as a network of excitatory and inhibitory neurons with a completely regular or a random connectivity pattern. However, neuroanatomy of the macaque and the cat cortex shows that cortical neurons are organized into densely linked groups that are sparsely and reciprocally interconnected. Interesting properties arise in the average activity of an ensemble of cortical neurons when the topology of the network itself is an intrinsic parameter of the model that can vary with a given set of rules. In this work we show that both the temporal activity and the encoded rhythms in an ensemble of cortical neurons depend on the topology of the network.

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References

  1. Chartrand, G.: Introductory Graph Theory. Dover Publications, Mineola New York (1985).

    Google Scholar 

  2. Sporns, O., Tononi, G., Edelman, G.M.: Relating Anatomical and Functional Con-nectivity in Graphs and Cortical Connection Matrices. Cerebral Cortex, Vol. 10. Oxford University Press, New York (2000) 127–141

    Google Scholar 

  3. White, J.G., Southgate, E., Thompson, J.N., Brenner, S.: The structure of the nervous system of the nematode Caenorhabditis elegans. Philosphical Transactions of the Royal Society of London. Series B 314 (1986) 1–340

    Google Scholar 

  4. Achacoso, T.B., Yamamoto, W.S.: AY’s Neuroanatomy of C.elegans for Computation. CRC Press, Boca Raton Florida (1992).

    Google Scholar 

  5. Lago L.F. Huerta R. Corbacho F. and Siguenza J.A. Fast Response and Temporal Coding on Coherent Oscillations in Small-world Networks, Physical Review Letters, 84 (12) (2000), 2758–2761.

    Article  Google Scholar 

  6. A. G. Phadke A.G. and Thorp J.S.: Computer Relaying for Power systems Wiley, New York, (1988).

    Google Scholar 

  7. Milgram S.: The Small World Problem, Psychology today, 2 (1967), 60–67.

    Google Scholar 

  8. Araújo T. and Vilela Mendes R.: Function and Form in Networks of Interacting Agents, Complex Systems 12 (2000) 357–378.

    MathSciNet  Google Scholar 

  9. Adini Y., Sagi D. and Tsodyks M. Excitatory-Inhibitory Network in the Visual Cortex: Psychophysical Evidence Proceedings of the National Academy of Sciences USA, 94 (1997) 10426–10431.

    Google Scholar 

  10. van Vreeswijk C. and Sompolinsky H., Chaotic Balanced State in a Model of Cortical Circuits Neural Comp. 10 (1998) 1321–1372.

    Google Scholar 

  11. Brunel, N., Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons Jour. of Computational Neuroscience 8 (2000) 183–208.

    Article  MATH  Google Scholar 

  12. Bernarder O, Koch C, Usher M, Synaptic background activity determines spatiotemporal integration in single pyramidal cells. Proceedings of the National Academy of Sciences USA, 88 (1991) 11569–11573.

    Google Scholar 

  13. C. Aguirre, F. Corbacho, R. Huerta, A realistic substrate for Small-world networks modeling: Proceedings of the 12th International Workshop on Database and Expert Systems Applications, IEEE Computer Society (2001).

    Google Scholar 

  14. C. Aguirre, R. Huerta, F. Corbacho P. Pascual, Analysis of biologically inspired Small-World networks: Artificial Networks-ICANN 2002, Lecture Notes in Computer Science, Springer (2002) 27–32.

    Google Scholar 

  15. Watts, D.J.: Small Worlds: The dynamic of Networks between Order and Randomness, Princeton University Press, Princeton, New Jersey (1999).

    Google Scholar 

  16. Bollobas, B.: Random Graphs. Harcourt Brace Jovanovich, Orlando Florida (1985).

    MATH  Google Scholar 

  17. Watts, D.J., Strogatz, S. H. Collective dynamics of small-world networks, Nature. 393 (1998) 440.

    Article  Google Scholar 

  18. Compte A., Sanchez-Vives M. V., McCormick D. A. and Wang X. J., Cellular and network mechanism of slow oscillatory activity in a cortical network model. Journal of Neurophysiology, In press (2003)

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Aguirre, C., Campos, D., Pascual, P., Serrano, E. (2003). Effiects of different connectivity patterns in a model of cortical circuits. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_11

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  • DOI: https://doi.org/10.1007/3-540-44868-3_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40210-7

  • Online ISBN: 978-3-540-44868-6

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