A novel method for the identification and modeling of neural networks using experimental spike trains is discussed. The method assumes a reference model of interconnected deterministic integrate-and-fire neurons and fit the parameters of the model to the observed experimental spike trains. The identification provides the properties of the individual synapses and neurons, hence extracting the fu...

This paper studies a simple nonautonomous circuit consisting of an RLC resonator, a dependent switch and a periodic pulse-train input. The circuit can exhibit chaotic behavior if an equidistant pulse-train input is applied. The dynamics can be analyzed by a mapping procedure based on a one-dimensional (1-D) return map focusing on the moments when the input is applied. If the periodic pulse-trai...

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This f...

Abstract—The paper deals with possibilities of increase train capacity by using a new type of railway wagon. In the first part is created a mathematical model to calculate the capacity of the train. The model is based on the main limiting parameters of the train maximum number of axles per train, maximum gross weight of train, maximum length of train and number of TEUs per one wagon. In the sec...

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This f...