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We study the influence of correlations among stochastic excitatory or
inhibitory inputs on the response of neuronal models.
Recently, the analysis reported in [1] for a current-driven Fitz-Hugh
Nagumo neuron has revealed that for any level of correlation
the emitted signal exhibits a maximal degree of regularity at some
finite noise intensity, i.e., a coherence resonance. Furthermore,
for either inhibitory or excitatory correlated stimuli a Double
Coherence Resonance (DCR) is observable. DCR refers to a (absolute)
maximum coherence in the output occurring for an optimal combination
of noise variance and correlation. We discuss the origin of these
effects and the possibility to observe similar phenomena in more
realistic setups. In particular, the response of conductance driven
neuronal models of physiological interest (like the Hodgkin-Huxley
neuron) subjected to correlated spike trains is analyzed.
[1] T. Kreuz, S. Luccioli, and A. Torcini, "Double coherence resonance in neuron models driven by discrete correlated noise", Phys. Rev. Lett. 97 (2006) 238101 |
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