Neural Networks for Processing of Biological Data
- Department of Biomedical Informatics
Artificial neural networks belong to one of alternative techniques for analysis and classification of data. Their main advantage is that there are able „to learn“ on the basis of examples, i.e. self-extract knowledge from available data. Further advantage is their generalization ability, i.e. ability of proper reaction on unknown stimulations. The subject is focused on basic biological motivation, simplified model of neuron and neural network, taxonomy of artificial neural networks. Individual and main paradigms of neural networks are discussed (simple neuron, multilayer perceptron network, RFB neural network, Hopfield network, Kohonen's self-organizing maps, ...). Preprocessing of data, parameter setting, convergence and so on is discussed in detail. The end of the subject is dedicated to computational means for artificial neural networks.
Exam: Written and oral.
- Syllabus of lectures:
- Biological motivation, basics of neural networks of living organisms. Transfer of signals, memory function.
- History of artificial neural networks (ANN), definition of ANN, taxonomy of ANN.
- Structure and function of neuron, network of neurons.
- Probabilistic ANN, generalized regression ANN.
- Multilayer perceptron network (MLP), method of learning/training, backpropagation, Levenberg-Marquart, delta-bar-delta. Features of MLP. Applications of MLP.
- RBF ANN, structure, features, learning, applications.
- Kohonen's self-organizing feature maps (SOFM), structure, features, learning, applications.
- Hopfield ANN, structure, features, learning, applications.
- Associative memories, structure, features, learning, applications.
- Modern ways of development of ANN, optic ANN, holographic ANN
- Computational means (toolboxes) for ANN (Neural Networks Toolbox in Matlab, Statistica Neural Networks)
- Syllabus of tutorials:
There are no training lessons.
- Study Objective:
The goal of the subject is to introduce the students basics of artificial neural networks, mainly basic paradigms of neural networks, their principles and features. The further goal is to show possibilities of neural networks for solving tasks from biomedical engineering.
- Study materials:
Rojas R: Neural Networks - A Systematic Introduction, Springer-Verlag, Berlin, New-York, 1996
Hassoun M.H.: Fundamentals of artificial neural networks, MIT Press, 1995
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: