Pena, Rodrigo F. O.
[1, 3, 4]
Shimoura, Renan O.
Kamiji, Nilton L.
Ceballos, Cesar C.
Borges, Fernando S.
Higa, Guilherme S. V.
De Pasquale, Roberto
Roque, Antonio C.
Total Authors: 9
 Univ Sao Paulo, Fac Philosophy Sci & Letters Ribeirao Preto, Dept Phys, Ribeirao Preto, SP - Brazil
 Univ Aix Marseille, Inst Neurosci Syst, UMR 1106, INS, Marseille - France
 Rutgers State Univ, Newark, NJ - USA
 New Jersey Inst Technol, Federated Dept Biol Sci, Newark, NJ - USA
 Oregon Hlth & Sci Univ, Vollum Inst, Portland, OR 97201 - USA
 Fed Univ ABC, Ctr Math Computat & Cognit, Sao Bernardo Do Campo, SP - Brazil
 SUNY Downstate Hlth Sci Univ, New York, NY - USA
 Univ Sao Paulo, Inst Biomed Sci, Dept Physiol & Biophys, Sao Paulo, SP - Brazil
Total Affiliations: 8
European Physical Journal-Special Topics;
Web of Science Citations:
Neurons in the nervous system are submitted to distinct sources of noise, such as ionic-channel and synaptic noise, which introduces variability in their responses to repeated presentations of identical stimuli. This motivates the use of stochastic models to describe neuronal behavior. In this work, we characterize an intrinsically stochastic neuron model based on a voltage-dependent spike probability function. We determine the effect of the intrinsic noise in single neurons by measuring the spike time reliability and study the stochastic resonance phenomenon. The model was able to show increased reliability for non-zero intrinsic noise values, according to what is known from the literature, and the addition of intrinsic stochasticity in it enhanced the region in which stochastic-resonance is present. We proceeded to the study at the network level where we investigated the behavior of a random network composed of stochastic neurons. In this case, the addition of an extra dimension, represented by the intrinsic noise, revealed dynamic states of the system that could not be found otherwise. Finally, we propose a method to estimate the spike probability curve from in vitro electrophysiological data. (AU)