Enhancement of EM Signal Detectability in a Realistic Model of Feedforward Neuronal Network

Abstract

Neuronal networks with feedforward architecture are typical of peripheral nervous system. A realistic stochastic model of feedforward network was here implemented and used to investigate the sensitivity of neuronal sensory pathways to input electromagnetic (EM) fields. Aim of this work was to address and characterize EM signal detectability throughout the network, pointing out the biophysical properties underlying possible signal amplification. Synaptic noise is shown to enhance signal transduction according to the stochastic resonance paradigm, and pooling neuron assemblies in a feedforward configuration is evidenced to give rise to amplification throughout the network layers. This may be relevant in a biomedical perspective, where techniques based on electric or magnetic stimulation of the nervous system could take advantage from signal transduction optimization.