Artificial Neuronal Networks

I Introduction.- 1 Neuronal Networks: Algorithms and Architectures for Ecologists and Evolutionary Ecologists.- II Artificial Neuronal Networks in Landscape Ecology and Remote Sensing.- 2 Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neuronal Networks.- 3 Soft Mapping of Coastal Vegetation from Remotely Sensed Imagery with a Feed-Forward Neuronal Network.- 4 Ultrafast Estimation of Neotropical Forest DBH Distributions from Ground Based Photographs Using a Neuronal Network.- 5 Normalized Difference Vegetation Index Estimation in Grasslands of Patagonia by ANN Analysis of Satellite and Climatic Data.- 6 On the Probabilistic Interpretation of Area Based Fuzzy Land Cover Mixing Proportions.- III Artificial Neuronal Networks in Population, Community and Ecosystem Ecology.- 7 Patterning of Community Changes in Benthic Macroinvertebrates Collected from Urbanized Streams for the Short Time Prediction by Temporal Artificial Neuronal Networks.- 8 Neuronal Network Models of Phytoplankton Primary Production.- 9 Predicting Presence of Fish Species in the Seine River Basin Using Artificial Neuronal Networks.- 10 Elucidation and Prediction of Aquatic Ecosystems by Artificial Neuronal Networks.- 11 Performance Comparison between Regression and Neuronal Network Models for Forecasting Pacific Sardine (Sardinops caeruleus) Biomass.- 12 A Comparison of Artificial Neuronal Network and Conventional Statistical Techniques for Analyzing Environmental Data.- IV Artificial Neuronal Networks in Genetics and Evolutionary Ecology.- 13 Application of the Self-Organizing Mapping and Fuzzy Clustering to Microsatellite Data: How to Detect Genetic Structure in Brown Trout (Salmo trutta) Populations.- 14 The Macroepidemiology of Parasitic and Infectious Diseases: A Comparative Study Using Artificial Neuronal Nets and Logistic Regressions.- 15 Evolutionarily Optimal Networks for Controlling Energy Allocation to Growth, Reproduction and Repair in Men and Women.- V Perspectives.- 16 Can Neuronal Networks be Used in Data-Poor Situations?.