Biological Cybernetics

, Volume 74, Issue 1, pp 73–83 | Cite as

High-order behaviour in learning gate networks with lateral inhibition

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Abstract

In this work we present a neural network model incorporating activity-dependent presynaptic facilitation with multidimensional inputs. The processing unit used is based on a slightly simplified version of the Learning Gate Model proposed by Ciaccia et al. (1992). The network topology integrates a well-known biological neural circuit with a lateral inhibition connection subnet. By means of simulation experiments, we show that the proposed networks exhibit basic and high-order features of associative learning. In particular, overshadowing and blocking are reproduced in the presence of both noise-free and noisy inputs. The role of noise in the development of high-order learning capabilities is also discussed.

Keywords

Network Model Network Topology Simulation Experiment Processing Unit Neural Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag 1996

Authors and Affiliations

  1. 1.C.I.O.C.-C.N.R. and Dipartimento di ElettronicaInformatica e Sistemistica, Università di BolognaBolognaItaly

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