Artificial neural network models

We outline the main models and developments in the broad field of artificial neural networks (ANN). A brief introduction to biological neurons motivates the initial formal neuron model - the perceptron. We then study how such formal neurons can be generalized and connected in network structures. Starting with the biologically motivated layered structure of ANN (feed-forward ANN), the networks are then generalized to include feedback loops (recurrent ANN) and even more abstract gen-eralized forms of feedback connections (recursive neuronal networks) enabling processing of structured data, such as sequences, trees, and graphs. We also introduce ANN models capable of forming topographic lower-dimensional maps of data (self-organizing maps). For each ANN type we out-line the basic principles of training the corresponding ANN models on an appropriate data collection.