This book contains the proceedings of the conference ANNIMAB-l, held 13-16 May 2000 in Goteborg, Sweden. The conference was organized by the Society for Artificial Neural Networks in Medicine and Biology (ANNIMAB-S), which was established to promote research within a new and genuinely cross-disciplinary field. Forty-two contributions were accepted for presentation; in addition to these, S invited papers are also included. Research within medicine and biology has often been characterised by application of statistical methods for evaluating domain specific data. The growing interest in Artificial Neural Networks has not only introduced new methods for data analysis, but also opened up for development of new models of biological and ecological systems. The ANNIMAB-l conference is focusing on some of the many uses of artificial neural networks with relevance for medicine and biology, specifically: . Medical applications of artificial neural networks: for better diagnoses and outcome predictions from clinical and laboratory data, in the processing of ECG and EEG signals, in medical image analysis, etc. More than half of the contributions address such clinically oriented issues. . Uses of ANNs in biology outside clinical medicine: for example, in models of ecology and evolution, for data analysis in molecular biology, and (of course) in models of animal and human nervous systems and their capabilities. . Theoretical aspects: recent developments in learning algorithms, ANNs in relation to expert systems and to traditional statistical procedures, hybrid systems and integrative approaches.

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Invited Presentations: Neural Computation in Medicine: Perspectives and Prospects; An Unsupervised Learning Method that Produces Organized Representations from Real Information; On Forgetful Attractor Network Memories; ART Neural Networks for Medical Data Analysis and Fast Distributed Learning; Discriminating Gourmets, Lovers and Enophiles? Neural Nets Tell All About Locusts, Toads and Roaches; Protein Beta-Sheet Partner Prediction by Neural Networks.- Medical Image Analysis: Cancerous Liver Tissue Differentiation Using LVQ; Quantification of Diabetic Retinopathy Using Neural Networks and Sensitivity Analysis; Internet Based Artificial Neural Networks for the Interpretation of Medical Images; Segmentation of Magnetic Resonance Images According to Contrast Agent Uptake Kinetics Using a Competitive Neural Network; Applications of Optimizing Neural Networks in Medical Image Registration; Detection of Features from Medical Images Using a Modular Network Approach that Relies on Learning by Sample; Neural Network Based Classification of Cell Images via Estimation of Fractal Dimensions.- Signal Processing in Medicine: Mutual Control Neural Networks for Sleep Arousal Detection; Extraction of Sleep-Spindles from the Electroencephalogram (EEG); Analyzing Brain Tumor Related EEG Signals with ICA Algorithms; Isolating Seizure Activity in the EEG with Independent Component Analysis; Seizure Detection with the Self-Organising Feature Map; Neural Network Approach to P Wave Detection in the Electrocardiogram; Graphical Analysis of Respiration in Postoperative Patients Using Self-Organising Maps.- Clinical Diagnosis and Medical Decision Support: Neural Network Predictions of Outcome from Posteroventral Pallidotomy; A Neural-Bayesian Approach to Survival Analysis; Identifying Discriminant Features in the Histopathology Diagnosis of Inflammatory Bowel Disease Using a Novel Variant of the Growing Cell Structure Network Technique; ClassifyingPigmented Skin Lesions with Machine Learning Methods; An Assessment System of Dementia of Alzheimer Type Using Artificial Neural Networks; A New Artificial Neural Network Method for the Interpretation of ECGs; Use of a Kohonen Neural Network to Characterize Respiratory Patients for Medical Intervention; determination of Microalbuminuria and Increased Urine Albumin Excretion by Immunoturbidimetric Assay and Neural Networks; Using an Artificial neural network to Predict Postoperative nausea and Vomiting; Acute Myocardial Infarction: Analysis of the ECH Using Artificial Neural Networks; Bayesian Neural Networks Used to Find Adverse Drug Combinations and Drug Related Syndromes; Monitoring of Physiological Parameters of Patients and Therapists During Psychotherapy Sessions Using Self-Organizing Maps.- Biomolecular Applications and Biological Modelling: Neuronal Network Modelling of the Somatosensory Pathway and its Application to General Anaesthesia; Electronic Noses and their Applications; A Hybrid Classification Tree and Artificial Neural Network Model for Predicting the In Vitro Response of the Human Immunodeficiency Virus (HIV1) to Anti-Viral Drug Therapy; Neural Unit Sensitive to Modulation; On Methods for Combination of Results from Gene-Finding Programs for Improved Prediction Accuracy; A Simulation Model for Activated Sludge Process Using Fuzzy Neural Networks; A General Method for Combining Predictors Tested on Protein Secondary Structure Prediction; A Three-Neuron Model of Information Processing During Bayesian Foraging; Sensorimotor Sequential Learning by an Artificial Neural Network based on Re-Defined Hebbian Learning; On Synaptic Plasticity: Modelling Molecular Kinases Involved in Transmitter Release; Self-Organizing Networks for Mapping and Clustering Biological Macromolecule Images; A Neural Network Model for Muscle Force Control Based on the Size Principle and Recurrent Inhibition of Renshaw Cells; Prediction of Photosensitizer Activity i