Abstract
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.
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