High-order behaviour in learning gate networks with lateral inhibition.
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.
Blanzieri
E
E
C.I.O.C.-C.N.R., Bologna, Italy.
Grandi
F
F
Maio
D
D
D000818:Animals; D003213:Conditioning, Psychological / Q000502:physiology; D003491:Cybernetics; D005108:Extinction, Psychological / Q000502:physiology; D006185:Habituation, Psychophysiologic / Q000502:physiology; D007858:Learning / Q000502:physiology*; D008433:Mathematics; D009415:Nerve Net; D009433:Neural Inhibition / Q000502:physiology*; D017603:Signal Detection, Psychological / Q000502:physiology; D013569:Synapses / Q000502:physiology