Abstract: There is described a method for computer-aided learning of a neural network, with a plurality of neurons in which the neurons of the neural network are divided into at least two layers, comprising a first layer and a second layer crosslinked with the first layer. In the first layer input information is respectively represented by one or more characteristic values from one or several characteristics, wherein every characteristic value comprises one or more neurons of the first layer. A plurality of categories is stored in the second layer, wherein every category comprises one or more neurons of the second layer. For one or several pieces of input information, respectively at least one category in the second layer is assigned to the characteristic values of the input information in the first layer.

Abstract: There is described a method for computer-aided learning of a neural network, with a plurality of neurons in which the neurons of the neural network are divided into at least two layers, comprising a first layer and a second layer crosslinked with the first layer. In the first layer input information is respectively represented by one or more characteristic values from one or several characteristics, wherein every characteristic value comprises one or more neurons of the first layer. A plurality of categories is stored in the second layer, wherein every category comprises one or more neurons of the second layer. For one or several pieces of input information, respectively at least one category in the second layer is assigned to the characteristic values of the input information in the first layer.

Abstract: There is described a method for computer-assisted processing of measured values detected in a sensor network, with the sensor network comprising a plurality of sensor nodes, which each feature one or more sensors for detection of the measured values, with the measured values of a number of adjacent sensor nodes being known in a sensor node. A multi-area neural network will be mapped onto a corresponding sensor network by the inventive method, which creates the opportunity, with the aid of the information from adjacent sensors, even with incorrect or failed measurements of a sensor node, of guaranteeing detection of a global situation at the location of the sensor node. A sensor network operated with such a method is in such cases more robust against the failure of a few sensors, since a corresponding measured value can be estimated in a suitable way, so that the measurement not available can be replaced by the estimated measured value.

Abstract: In a method, arrangement and computer program for analysis of neuronal activities in neuronal areas, signals are recorded, with each signal describing the neuronal activity in one of the neuronal areas. A matchable coupling forms the basis of all signals, described by the use of matchable coupling parameters that describe the statistical relationship between the signals. Probabilities for an occurrence of the signals are determined, whereby a statistical distribution is the basis of the signals. The matchable coupling parameters are determined by optimization of the probabilities, hence matched and analyzed.

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: In a pattern recognition system, a pattern which is to be recognized later is prescribed in a learning phase. This pattern is detected sequentially, that is to say the informative areas of the pattern are detected and, moreover, the spatial relationship between the areas is also stored. In the recognition phase, a hypothesis which indicates a presumed pattern and, furthermore, indicates where such further prominent areas should be located in the pattern to be recognized if the presumption is correct, is generated on the basis of the acquired data of a first area of a pattern to be recognized, and on the basis of the stored data. Thus, patterns are learned through their location information, on the one hand, and through their spatial relationship to each other, on the other hand, stored and then re-recognized.

Abstract: The activity of a pharmaceutical preparation or medicament on a neuronal structure is analyzed by subjecting a neuronal structure to the influence of a pharmaceutical preparation. Signals describing neuronal activities in the neuronal structure under the influence of the pharmaceutical preparation are detected and statistically evaluated to determine indicators for the neuronal structure under the influence of the pharmaceutical preparation. The indicators describe the activity of the pharmaceutical preparation.

Abstract: An arrangement and method are presented that enable a prediction of an abnormality and implement a suitable action opposing the abnormality. An information flow underlying a dynamic system is interpreted and a prediction quantity that comprises the abnormality as characterizing quantity of the dynamic system is determined from it. A neural network is trained with measured data of the system. After the training, the abnormality can be indicated on the basis of the prediction quantity before it occurs and the occurrence can be opposed with suitable measures.

Abstract: The present invention relates to a system and a method for recognizing a prescribed object, having a storage device for storing attribute information relating to the prescribed object, detecting means for detecting attributes in a detection range and for outputting corresponding detection information, first processing means for processing, in parallel and separately for each possible attribute type, the detection information for the detection means by using the attribute information from the storage device and for outputting corresponding processing information, and second processing means for processing the process information from the first processing means and for outputting the information for determining the position of the prescribed object in the detection range.

Abstract: In a method, arrangement and computer program for analysis of neuronal activities in neuronal areas, signals are recorded, with each signal describing the neuronal activity in one of the neuronal areas. A matchable coupling forms the basis of all signals, described by the use of matchable coupling parameters that describe the statistical relationship between the signals. Probabilities for an occurrence of the signals are determined, whereby a statistical distribution is the basis of the signals. The matchable coupling parameters are determined by optimization of the probabilities, hence matched and analyzed.

Abstract: A device for context-dependent data analysis has a plurality of neurons which are combined to form a plurality of neuron pools. The weights of the links between two neurons are a function of the neuron pools to which the two neurons belong.

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: Neuronal activities in neuronal areas are analyzed using a coupling model in which coupling model a) the neuronal activities and signals are interconnected by using cross-coupling variables, b) the signals are connected by using signal coupling variables that in each case interconnect two of the signals, c)the neuronal activities are connected by using activity coupling variables that in each case interconnect two of the neuronal activities, in which case at least the signal coupling variables are determined for the analysis when optimizing.

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: The model is a third generation neurosimulator. It has a plurality of areas whose functions can be identified with the functions of the areas of the dorsal and ventral path of the visual cortex of the human brain. Feedback is provided between different areas during processing. There is additionally provided competition for attention between different features and/or different spatial regions. The model is very flexibly suitable for image processing. It simulates natural human image processing and explains many experimentally observed phenomena.

Abstract: For determining an object in an image, hierarchical partial areas and sub-partial areas are selected, which are recorded with different resolution on each hierarchical level and which are compared with features of the object to be identified. If the object is identified with a sufficient level of certainty, the object to be identified is output as an identified object. If this is not the case, an additional sub-partial area of the current partial area is selected, and information with an, in turn, increased local resolution is detected from said sub-partial area.

Abstract: A time series that is established by a measured signal of a dynamic system, for example a quotation curve on the stock market, is modelled according to its probability density in order to be able to make a prediction of future values. A non-linear Markov process of the order m is suited for describing the conditioned probability densities. A neural network is trained according to the probabilities of the Markov process using the maximum likelihood principle, which is a training rule for maximizing the product of probabilities. The neural network predicts a value in the future for a prescribable number of values m from the past of the signal to be predicted. A number of steps in the future can be predicted by iteration. The order m of the non-linear Markov process, which corresponds to the number of values from the past that are important in the modelling of the conditioned probability densities, serves as parameter for improving the probability of the prediction.

Abstract: A method for determining conditioned entropies for a prescribable plurality of future sampling times for a set of samples based upon an information flow. A classification of a time series is implemented on the basis of the information flow. The information flow reflects nonlinear correlations between the samples. A classification is thus possible between those time series whose samples are non-linearly correlated and those time series whose samples are stochastically independant.

Abstract: A method for classifying a time series, that includes a prescribable plurality of samples, with a computer wherein generalized correlation integral is determined for at least a part of samples of a time series. A functions family of an entropy function is determined from the values of the generalized correlation integral. A plurality of considered future samples is thereby employed as a family parameter of the functions family. The time series is classified into various types of characteristic processes from the curve of the functions family of the entropy function.