This paper presents a nonlinear model of human brain activity in response to visual stimuli according to Blood-Oxygen-Level-Dependent (BOLD) signals scanned by functional Magnetic Resonance Imaging (fMRI). A BOLD signal often contains a low frequency signal component (trend), which is usually removed by detrending because it is considered a part of noise. However, such detrending could destroy the dynamics of the BOLD signal and ignore an essential component in the response. This paper shows a model that, in the absence of detrending, can predict the BOLD signal with smaller errors than existing models. The presented model also has low Schwarz information criterion, which implies that it will be less likely to overfit the experimental data. Comparison between the various types of artificial trends suggests that the trends are not merely the result of noise in the BOLD signal.

This paper studies the propagation and crossing of signals in cellular automata whose cells are updated at random times. The signals considered consist of a core part, surrounded by an insulating sheath that is missing at the side of the core that corresponds to the direction into which the signal moves. We study two types of signals: (1) signals by which the sheath at the left and right sides of the core advance first in a propagation step, followed by the core, and (2) signals by which the core advances first, followed by the sheath at its left and right sides. These types naturally arise in, respectively, Moore neighborhood cellular automata with semi-totalistic rules and von Neumann neighborhood cellular automata with symmetric transition rules. The type of a signal has a profound impact on the way signals cross each other, as we show by the construction of one signal of each type. The results we obtained should be of assistance in constructing asynchronous circuits on asynchronous cellular automata.

The relationship between different brain areas is characterized by functional networks through correlations of time series obtained from neuroimaging experiments. Due to its high spatial resolution, functional MRI data is commonly used for generating functional networks of the entire brain. These networks are comprised of the measurement points/channels as nodes and links are established if there is a correlation in the measured time series of these nodes. However, since the evaluation of correlation becomes more accurate with the length of the underlying time series, we construct in this paper functional networks from MEG data, which has a much higher time resolution than fMRI. We study in particular how the network topologies change in an experiment on ambiguity of words, where the subject first receives a priming word before being presented with an ambiguous or unambiguous target word.

In this paper, we survey the state of the art of the secure key exchange method that is secured by the laws of classical statistical physics, and involves the Kirchhoff's law and the generalized Johnson noise equation, too. We discuss the major characteristics and advantages of these schemes especially in comparison with quantum encryption, and analyze some of the technical challenges of its implementation, too. Finally, we outline some ideas about how to use already existing and currently used wire lines, such as power lines, phone lines, internet lines to implement unconditionally secure information networks.

In this article a Neural Network learning scheme is described, which is a generalization of VQ (Vector Quantization) and ART2a (a simplified version of Adaptive Resonance Theory 2). The basic differences between VQ and ART2a will be exhibited and it will be shown how these differences are covered by the generalized scheme. The generalized scheme enables a rich set of variations on VQ and ART2a. One such variation uses the expression ||I||2+||zj||2/||zj||sin(I,zj), as the distance measure between input vector I and weight vector zj. This variation tends to be more robust to noise than ART2a, as is shown by experiments we performed. These experiments use the same data-set as the ART2a experiments in Ref.(3).