Vesicular mechanisms and estimates of firing probability in a network of spiking neurons

Physica D: Nonlinear Phenomena

Volumn 181, Issue 1-2, 2003, Pages 132-145

  Rodriguez, Roger ^a   Lansky, Petr ^b   Di Maio, Vito ^c  

^a CENTRE DE PHYSIQUE THÉORIQUE   (France)

^b INSTITUTE OF PHYSIOLOGY   (Czech Republic)

^c CNR   (Italy)

Author keywords

Conductance based neuronal model; Diffusion approximation; Inhomogeneous Poisson process; Network; Vesicle

Indexed keywords

COMPUTER SIMULATION; DIFFUSION; PROBABILITY; SYNCHRONIZATION; TRANSMITTERS;

VESICULAR MECHANISMS;

NEURAL NETWORKS;

EID: 0038347364     PISSN: 01672789     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-2789(03)00093-9     Document Type: Article

References (22)

1. Bennett M.R., Kearns J.L., Statistics of transmitter release at nerve terminals. Prog. Neurobiol. 60:2000;545.
2. Faure P., Kaplan D., Korn H., Synaptic efficacy and the transmission of complex firing patterns between neurons. J. Neurophysiol. 84:2000;3010.
3. Auger C., Marty A., Quantal currents at single-site central synapses. J. Physiol. (London). 526:2000;3.
4. Ventriglia F., Di Maio V., A Brownian simulation model of glutamate synaptic diffusion in the femtosecond time scale. Biol. Cybern. 83:2000;93.
5. Matveev V., Wang X., Implications of all-or-none synaptic transmission and short-term depression beyond vesicle depletion: a computational study. J. Neurosci. 20:2000;1575.
6. W. Gerstner, W.M. Kistler, Spiking Neuron Models, Cambridge University Press, Cambridge, 2002.
7. Shimokawa T., Pakdaman K., Takahata T., Tanabe S., Sato S., A first passage-time analysis of the periodically forced noisy leaky integrate-anf-fire model. Biol. Cybern. 83:2000;327.
8. Bressloff P.C., Dynamics of compartmental model integrate-and-fire neuron with somatic potential reset. Physica D. 80:1995;399.
9. Lansky P., Rodriguez R., Two-compartment stochastic model of a neuron. Physica D. 132:1999;267.
10. Rodriguez R., Lansky P., A simple stochastic model of spatially complex neurons. Biosystems. 58:2000;49.
11. Hodgkin A.L., Huxley A.F., A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. (London). 117:1952;500.
12. Kepler T.B., Abbott L.F., Marder E., Reduction of conductance-based neuron models. Biol. Cybern. 66:1992;381.
13. Morris C., Lecar H., Voltage oscillations in the barnacle giant muscle fiber. Biophys. J. 35:1981;195.
14. J. Rinzel, G.B. Ermentrout, Analysis of neural excitability and oscillations, in: C. Koch, I. Segev (Eds.), Methods of Neural Neuronal Modelling: From Synapses to Networks, MIT Press, Cambridge, MA, 1989 (revised 1998).
15. H.C. Tuckwell, Introduction to Theoretical Neurobiology, Cambridge University Press, Cambridge, 1988.
16. Tuckwell H., Rodriguez R., Analytical and simulation results for stochastic Fitzhugh-Nagumo neurons and neural networks. J. Comput. Neurosci. 5:1998;91.
17. Lindner B., Schimansky-Geier L., Analytical approach to the stochastic FitzHugh-Nagumo system and coherence resonance. Phys. Rev. E. 60:1999;7270.
18. Lecar H., Nossal R., Theory of threshold fluctuations in nerves I, II. Biophys. J. 11:1971;1048.
19. Blanchard P., Combe P., Nencka H., Rodriguez R., Stochastic dynamical aspect of neuronal activity. J. Math. Biol. 31:1993;189.
20. Ingber L., Statistical mechanics of neocortical interactions. Physica D. 5:1982;83.
21. Lansky P., On approximations of Stein's neuronal model. J. Theoret. Biol. 107:1984;631.
22. L. Ricciardi, Diffusion Processes and Related Topics in Biology, Springer, Berlin, 1977.