Despite intensive research, the mechanisms by which neurons encode information in spike trains remain poorly understood. Recent work has focused on how a single neuron (modeled using the FitzHugh-Nagumo model) encodes a weak (subthreshold) sinusoidal signal, in a noisy environment , and on the impact of a second neuron, which does not perceive the signal . By applying a symbolic time-series analysis method to the sequence of inter-spike-intervals (ISIs), preferred and infrequent spike patterns were detected, whose probabilities encode information of both, the amplitude and the frequency of the weak signal.
Here we show that this symbolic information-encoding mechanism is robust when working with ring networks of N elements (we assume that the weak signal is perceived by all neurons) and different topologies (local, non-local and all-to-all topologies). We first demonstrate that probabilities encode information about modulation and period in a similar way they did for 1 and 2 elements for all topologies. Second, we show that for low noise amplitudes probabilities depend on the number of elements and on the topology, yet for intermediate noise do not. As a final step, we analyze how the coupling parameters, the network size and its topology impact on the synchronization.
 J. A. Reinoso, M. C. Torrent, C. Masoller Emergence of spike correlations in periodically forced excitable systems, Phys. Rev. E 94, 032218 , 2016. M. Masoliver, C. Masoller, Subthreshold signal encoding in coupled FitzHugh-Nagumo neurons, Scientific Reports, 8 8276, 2018.