title: Optimal percentage of inhibitory synapses in multi-task learning.
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.
authors.affiliation: Physics Department, University of Naples Federico II, Napoli, Italy.
authors.forename: Vittorio
authors.initials: V
authors.lastname: Capano
authors.affiliation: 1] Institute Computational Physics for Engineering Materials, ETH, Zürich, CH [2] Departamento de Fisica, Universidade Federal do Ceará, 60451-970 Fortaleza, Ceará, Brazil.
authors.forename: Hans J
authors.initials: HJ
authors.lastname: Herrmann
authors.affiliation: Department of Industrial and Information Engineering, Second University of Naples and INFN Gr. Coll. Salerno, Aversa (CE), Italy.
authors.forename: Lucilla
authors.initials: L
authors.lastname: de Arcangelis
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology; D007858:Learning; D008959:Models, Neurological; D013569:Synapses / Q000502:physiology*