Influence of the refractory period on neural networks based on the recognition of neural signatures

Experimental evidence has revealed that different living neural systems can “sign” their output signals with some specific neural signature. Although experimental and modeling results suggest that these neural signatures can have significant implications for the activity of the neural circuits where they are present, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the source of individual signals and contextualize incoming messages as a function of this identification can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to encode and process information based on the emission and recognition of specific neural fingerprints. In this paper, we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.