The study of artificial neural map formation provides useful tools for biological and statistical data modelling. "Artificial Neural Maps" offers a systematic survey of extended neural map concepts and architectures. In the first part of this work, several new extended neural map architectures are introduced. This kind of modularity has been observed in the organization of the human cortex, where sensory, computational and motor maps are arranged in a similar fashion. This modular approach provides increased flexibility in customizing architectures to specific problems. Example applications include: system identification, vibration control, gene expression analysis, inverse kinematics of robots, biomedical signal visualization and plant recognition. In the second part of this work new neural network based techniques are presented for performing multi- sensor data fusion. The presented techniques are applied in several data fusion applications where heterogeneous sensor data are available. The target group includes neural network researchers, industrial scientists involved in data mining and researchers in biosystems engineering.

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