  Recent studies have proposed that the diffusion of messenger molecules, such
as monoamines, can mediate the plastic adaptation of synapses in supervised
learning of neural networks. Based on these findings we developed a model for
neural learning, where the signal for plastic adaptation is assumed to
propagate through the extracellular space. We investigate the conditions
allowing learning of Boolean rules in a neural network. Even fully excitatory
networks show very good learning performances. Moreover, the investigation of
the plastic adaptation features optimizing the performance suggests that
learning is very sensitive to the extent of the plastic adaptation and the
spatial range of synaptic connections.
