Performing more tasks in parallel is a typical feature of complex brains.
These are characterized by the coexistence of excitatory and inhibitory
synapses, whose percentage in mammals is measured to have a typical value of
20-30\%. Here we investigate parallel learning of more Boolean rules in
neuronal networks. We find that multi-task learning results from the
alternation of learning and forgetting of the individual rules. Interestingly,
a fraction of 30\% inhibitory synapses optimizes the overall performance,
carving a complex backbone supporting information transmission with a minimal
shortest path length. We show that 30\% inhibitory synapses is the percentage
maximizing the learning performance since it guarantees, at the same time, the
network excitability necessary to express the response and the variability
required to confine the employment of resources.Comment: 5 pages, 5 figure
