Apicella, I.Busiello, D. M.Scarpetta, S.Suweis, S.2021-10-232021-10-232021-10-232021-10-2110.1016/j.neucom.2020.04.162https://infoscience.epfl.ch/handle/20.500.14299/182478WOS:000703158300005Neuromorphic networks can be described in terms of coarse-grained variables, where emergent sus-tained behaviours spontaneously arise if stochasticity is properly taken into account. For example it has been recently found that a directed linear chain of connected patch of neurons amplifies an input sig-nal, also tuning its characteristic frequency. Here we study a generalization of such a simple model, intro-ducing heterogeneity and variability in the parameter space and long-range interactions, breaking, in turn, the preferential direction of information transmission of a directed chain. On one hand, enlarging the region of parameters leads to a more complex state space that we analytically characterise; moreover, we explicitly link the strength distribution of the non-local interactions with the frequency distribution of the network oscillations. On the other hand, we found that adding long-range interactions can cause the onset of novel phenomena, as coherent and synchronous oscillations among all the interacting units, which can also coexist with the amplification of the signal. (c) 2021 Published by Elsevier B.V.Computer Science, Artificial IntelligenceComputer Sciencesynchronisationstochastic resonancestochastic amplificationlong-range connectionswilson-cowan modelneuronal avalanchestext::journal::journal article::research article