Abstract: A method for evaluating an image (fMRI-image) of the brain that has been obtained by functional magnetic resonance tomography is provided. According to the method, a neuronal network is used to simulate the activities of the brain. Supposed disorders of the brain are simulated in the neuronal network (as a disturbed neuronal network). The activities determined in the brain can be artificially simulated in the model and its effect on the complex synergy of the areas of the brain can be quantified. The comparison with the fMRI image or fMRI activity pattern relating to the patient enables the cause of the disorders to be localized, thus leading to a successful diagnosis.

Abstract: A method classifies the traffic dynamism of a network communication using a network that contains pulsed neurons. Traffic data of the network communication are used as the input variables of the neuronal network. Temporal clusters obtained by processing the pulses are used as the output variables of the neuronal network. The traffic dynamism is classified by a synaptic model whose dynamism depends directly on the exact clocking of pre- or post-synaptic pulses.

Abstract: In an arrangement and a method for the determination and description of the transmission behavior of a nerve cell, using artificial neurons, a first artificial neuron describes an exciter nerve cell and has a first input to which a first input signal is supplied representing external synaptic activity, a second input to which a second input signal is supplied representing internal synaptic activity, and an output at which an output signal, representing action potential activity, is emitted. A second artificial neuron generates the second input signal corresponding to internal synaptic activity.

Abstract: In a computerized method for classification of a time series containing a prescribable number of samples, such as an electrical signal, a parameterized, dynamic set is determined in the computer from the samples in the time series, the set identifying non-linear correlations between the samples of the time series. A classification of the time series is implemented in the computer on the basis of the parameterized, dynamic multiplicity.

Abstract: Optimum values for manipulated variables s.sub.1, . . . ,s.sub.S of a technical system (.phi.) are determined for prescribed operational variables b.sub.1, . . . ,b.sub.B in order to optimize a prescribed target function z=g(y.sub.2. . . ,y.sub.N) of system properties y.sub.2, . . . , y.sub.N where y.sub.i =.phi..sub.i (s.sub.1, . . . ,s.sub.S,b.sub.1, . . . ,b.sub.B). Here, a set of functions f.sub.i (w.sub.1, . . . ,w.sub.W,x.sub.1, . . . ,x.sub.S=B) is used whose parameters w.sub.1, . . . ,w.sub.W are set in such a way that the functions f.sub.i model the system in such a way that the functions f.sub.i approximate the system functions .phi.i as functions of their variables x.sub.1, . . . ,x.sub.S+B in terms of an interval of prescribed magnitude; the manipulated variables s.sub.1, . . . ,s.sub.S are determined by optimizing the function g(f.sub.1 (w.sub.1, . . . ,w.sub.W,s.sub.1, . . . ,s.sub.S,b.sub.1, . . . ,b.sub.B), . . . , f.sub.N (w.sub.1, . . . ,w.sub.W,s.sub.1, . . . ,s.sub.S,b.sub.1, . . . ,b.sub.