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: An equivalence relationship is created between a) the functional network of the genome and proteome and b) a neuronal network. Both networks represent highly cross-linked feedback systems. The equivalence relationship makes it possible to model the functional network of proteins of and genes by an equivalent artificial neuronal network. The dynamic interaction of genes and regulatory proteins is modeled by a dynamic neuronal network. The method uses information obtained in a temporal sequence of gene expression patterns for identification of causal regulatory correlations, thereby enabling target proteins to be identified on a systematic basis.

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 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: 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: In order to identify pharmaceutical targets, at least one correlation between the expression rates of different genes of a cell is ascertained by evaluating a plurality of gene expression patterns. In this case, correlations of second or higher order are considered. The correlations make it possible to infer causal relationships between different genes and the associated proteins. The regulatory network of the cell being studied can be therefore deduced from the correlations. Suitable targets can be identified from the regulatory network which has been deduced in such a way.

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.