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.

神经系统中的神经元受到不同噪声源的影响，例如离子通道和突触噪声，这会导致它们对重复呈现相同刺激的反应发生变化。这促使使用随机模型来描述神经元行为。在这项工作中，我们描述了一个基于电压相关尖峰概率函数的内在随机神经元模型。我们通过测量尖峰时间可靠性并研究随机共振现象来确定单个神经元中固有噪声的影响。根据文献中已知的内容，该模型能够显示非零固有噪声值的可靠性增加，并且在其中添加固有随机性增强了存在随机共振的区域。我们继续在网络层面进行研究，研究由随机神经元组成的随机网络的行为。在这种情况下，由固有噪声表示的额外维度的添加揭示了系统的动态状态，否则无法找到。最后，我们提出了一种从体外电生理数据估计尖峰概率曲线的方法。