Abstract. This paper presents a new approach to mental functions modeling with the use of artificial neural networks. The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly inspired by the biological neuron. On the other hand, the classical paradigms of artificial neural networks are not suitable because they simplify too much the real processes in biological neural network.The search for a compromise between the complexity of biological neural network and the practical feasibility of the artificial network led to a new learning algorithm. This algorithm is based on the classical multilayered neural network; however, the learning rule is different. The neurons are updating their parameters in a way that is similar to real biological processes. The basic idea is that the neurons are competing for resources and the criterion to decide which neuron will survive is the usefulness of the neuron to the whole neural network. The neuron is not using "teacher" or any kind of superior system, the neuron receives only the information that is present in the biological system.The learning process can be seen as searching of some equilibrium point that is equal to a state with maximal importance of the neuron for the neural network. This position can change if the environment changes. The name of this type of learning, the homeostatic artificial neural network, originates from this idea, as it is similar to the process of homeostasis known in any living cell. The simulation results suggest that this type of learning can be useful also in other tasks of artificial learning and recognition.

The attention level of car drivers is affected by many factors. Music is one of the most importantones, but its effect is rarely studied. Music can affect driving style in both positive and negative ways, as itcan reduce fatigue but also increase the level of distraction or aggression. This article presents anexperimental investigation of the effects of music on driver attention level. Several measurements on avehicle simulator were done to collect data that demonstrates the relationship between music and theperformance of the car driver. The simulation measured performance under three conditions - relaxationmusic, rock music and silence. Additionally, the measurements were repeated in both fresh and tired states.The results are, in some aspects, different from our expectations - for example, relaxation music improvedreaction time but also correlated with a higher occurrence of inappropriate steering actions. Deeperunderstanding of how the music and noise affect the driver’s actions and decisions will help to improveroad safety and reduce the probability of accidents

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This article presents an improvement of learning algorithm for an artificial neural network that makes the learning process more similar to a biological neuron, but still simple enough to be easily programmed. This idea is based on autonomous artificial neurons that are working together and at same time competing for resources; every neuron is trying to be better than the others, but also needs the feed back from other neurons. The proposed artificial neuron has similar forward signal processing as the standard perceptron; the main difference is the learning phase. The learning process is based on observing the weights of other neurons, but only in biologically plausible way, no back propagation of error or 'teacher' is allowed. The neuron is sending the signal in a forward direction into the higher layer, while the information about its function is being propagated in the opposite direction. This information does not have the form of energy, it is the observation of how the neuron's output is accepted by the others. The neurons are trying to find such setting of their internal parameters that are optimal for the whole network. For this algorithm, it is necessary that the neurons are organized in layers. The tests proved the viability of this concept-the learning process is slower; but has other advantages, such as resistance against catastrophic interference or higher generalization.