K�nstliche neuronale Netze

Abstract

Artificial neural networks (ANN) are constructed to simulate processes of the central nervous system of higher creatures. An ANN consists of a set of processing units (nodes) which simulate neurons and are interconnected via a set of "weights" (analogous to synaptic connections in the nervous system) in a way which allows signals to travel through the network in parallel. The nodes (neurons) are simple computing elements. They accumulate input from other neurons by means of a weighted sum. If a certain threshold is reached the neuron sends information to all other connected neurons otherwise it remains quiescent. One major difference compared with traditional statistical or rule-based systems is the learning aptitude of an ANN. At the very beginning of a training process an ANN contains no explicit information. Then a large number of cases with a known outcome are presented to the system and the weights of the inter-neuronal connections are changed by a training algorithm designed to minimise the total error of the system. A trained network has extracted rules that are represented by the matrix of the weights between the neurons. This feature is called generalisation and allows the ANN to predict cases that have never been presented to the system before. Artificial neural networks have shown to be useful predicting various events. Especially complex, non-linear, and time depending relationships can be modelled and forecasted. Furthermore an ANN can be used when the influencing variables on a certain event are not exactly known as it is the case in financial or weather forecasts. This article aims to give a short overview on the function of ANN and their previous use and possible future applications in anaesthesia, intensive care, and emergency medicine.

Semi-Automatic Classification of Skeletal Morphology in Genetically Altered Mice Using Flat-Panel Volume Computed Tomography

“…Although, other statistical methods might be appropriate to analyze these complex biological relationships, we have chosen neuronal networks for the classification process because of several advantages of this method. Neuronal networks are able to elucidate nonlinear problems and learn by example, so the details of the complex morphological skull structure on the basis of which the mice classification is made, are not needed [12,13]. Therefore, the use of neuronal networks is highly favorable in our study, characterized by poorly conditioned features space and the comparison of mice types, in which the distinct alterations between the different skull phenotypes are not known yet.…”

“…Neuronal networks learn by example so the details of how to recognize the phenotype of the skull are not needed. What is needed is a set of examples that are representative of all the variations of the phenotype [12,13]. Such neuronal networks have already been applied to characterize the variability of anthropological features of the human nasal skeleton [14] and to analyze and classify human craniofacial growth [15].…”