2020/01/22 by Ilenia Apicella, Daniel Maria Busiello, Apicella, Ilenia +5
Neuroscience · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #Advanced Thermodynamics and Statistical Mechanics #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Neural dynamics and brain function #cond-mat.dis-nn #nlin.AO #stochastic dynamics and bifurcation
paper · pdf · doi:10.48550/arxiv.2001.07913
arxiv created 2020/01/22 · openalex publication_date 2020/01/22 · arxiv updated 2020/01/23 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Neuromorphic networks can be described in terms of coarse-grained variables, where emergent sustained behaviours spontaneously arise if stochasticity is properly taken in account. For example it has been recently found that a directed linear chain of connected patch of neurons amplifies an input signal, also tuning its characteristic frequency. Here we study a generalization of such a simple model, introducing 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.