Artificial neural network models

Peter Tino*, Lubica Benuskova, Alessandro Sperduti

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

15 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationSpringer Handbook of Computational Intelligence
EditorsJanusz Kacprzyk , Witold Pedrycz
Number of pages17
ISBN (Electronic)9783662435052
ISBN (Print)9783662435045
Publication statusPublished - 1 Jan 2015

ASJC Scopus subject areas

  • General Computer Science


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