SELECTIVE PATTERN RECALL IN NEURAL NETWORKS BY CHEMICAL MODULATION

The authors present a simple neural network model in which the outputs of groups of neurons are chemically modulated. This feature takes into account the existence of neuromodulators which are known to have a strong effect on the behaviour of real biological networks. The effective (non-symmetric) connection matrix thereby becomes a function of the modulator concentrations. By deriving evolution equations for the pattern overlaps they show that, during recall, the system can select distinct subclasses from the stored patterns by varying these concentrations. This model may increase the understanding of the function of neuromodulators.