Leaky integrate and fire models coupled through copulas: Association properties of the interspike intervals

Chinese Journal of Physiology

Volumn 53, Issue 6, 2010, Pages 396-406

  Sacerdote, Laura ^a   Tamborrino, Massimiliano ^b  

^a UNIVERSITY OF TURIN   (Italy)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Copulas; Interspike interval dependency; Leaky integrate and fire models

Indexed keywords

ARTICLE; CELL ACTIVITY; CORRELATION ANALYSIS; LEAKY AND INTEGRATE FIRE MODEL; MATHEMATICAL COMPUTING; NERVE CELL MEMBRANE POTENTIAL; NERVE CELL NETWORK; SPATIAL AUTOCORRELATION ANALYSIS; STATISTICAL MODEL;

EID: 79955385653     PISSN: 03044920     EISSN: 26660059     Source Type: Journal    
DOI: 10.4077/CJP.2010.AMM028     Document Type: Article

References (17)

1. Billingsey, P. Probability and Measure. New York: John Wiley & Sons, 1995.
2. Borisyuk, G.N., Borisyuk, R.M., Kirillov, A.B., Kovalenko, E.I. and Kryukov, V.I. A new statistical method for identifying interconnections between neuronal network elements. Biol. Cybern. 52: 301-306, 1985.
3. Brillinger, D.R. Condence intervals for the crosscovariance function. Selecta: Statistica Canadiana V: 1-16, 1979.
4. Burkitt, A.N. A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biol. Cybern. 95: 119, 2006.
5. Burkitt, A.N. A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties. Biol. Cybern. 95: 97-112, 2006.
6. Genest, C., Rémillard, B. and Beaduoin, D. Goodness-of-fit tests for copulas: A review and a power study. Insurance: Math. Econ. 44: 199-213, 2009.
7. Iglesias, J. and Villa, A.E.P. Emergence of preferred firing sequences in large spiking neuronal networks during simulated neuronal development. Int. J. Neural. Syst. 18: 267-277, 2008.
8. Jenison, R.L. and Reale, R.A. The shape of neural dependence. Neural Comput. 16: 665-672, 2004.
9. Jolivet, R., Roth, A., Schurmann, F., Gerstner, W. and Senn, W. Special issue on quantitative neuron modeling. Biol. Cybern. 99: 237-239, 2009.
10. Kallianpur, G. On the diffusion approximation to a discontinuous model for a single neuron. In: Contributions to Statistics, edited by Sen, P.K. Amsterdam: North-Holland, pp. 247-258, 1983.
11. Kloeden, P. and Platen, P. The Numerical Solution of Stochastic Differential Equations. Berlin: Springer Verlag, 1992.
12. Nelsen, R. An Introduction to Copulas. New York, NY: Springer, 2006.
13. Perkel, D.H., Gerstein, G.L. and Moore, G.P. Neuronal spike trains and stochastic point processes. I. The single spike train. Biophys. J. 7: 391-418, 1967.
14. Perkel, D.H., Gerstein, G.L. and Moore, G.P. Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophys. J. 7: 419-440, 1967.
15. Ricciardi, L.M. Diffusion Processes and Related Topics in Biology. Lecture Notes in Biomathematics. Berlin: Springer Verlag, vol. 14, 1977.
16. Sacerdote, L. and Sirovich, R. A copulas approach to neuronal networks models. J. Physiol. Paris, 103: 223-230, 2010.
17. Sacerdote, L., Sirovich, R. and Villa, A.E.P. Effect of increasing inhibitory inputs on information processing within a small network of spiking neurons. Lect. Notes Comput. Sci. 4507: 23-30, 2007