Abstract
We investigate the synchronization features of a network of spiking neurons under a distance-dependent coupling following a power-law model. The interplay between topology and coupling strength leads to the existence of different spatiotemporal patterns, corresponding to either non-synchronized or phase-synchronized states. Particularly interesting is what we call synchronization malleability, in which the system depicts significantly different phase synchronization degrees for the same parameters as a consequence of a different ordering of neural inputs. We analyze the functional connectivity of the network by calculating the mutual information between neuronal spike trains, allowing us to characterize the structures of synchronization in the network. We show that these structures are dependent on the ordering of the inputs for the parameter regions where the network presents synchronization malleability and we suggest that this is due to a complex interplay between coupling, connection architecture, and individual neural inputs.
Highlights
Modeling of natural phenomena through the use of coupled networks finds applications in various scientific areas [1,2]
III A); (ii) evaluate the firing rate of each neuron and evaluate the mean firing rate of each bin, or group of neurons; (iii) we evaluate the standard deviation over all bins and obtain the coefficient of variability (CV); (iv) test for different numbers of neurons in each group
Especially in the Throughout this paper, we have analyzed a network composed of 525 spiking neurons simulated with the Chialvo map following a connection architecture described by a distance-dependent power-law scheme
Summary
Modeling of natural phenomena through the use of coupled networks finds applications in various scientific areas [1,2]. Long-range and global connection schemes do facilitate synchronization processes [26] To analyze these phenomena, a coupling architecture given by a distance-dependent power-law scheme can be used. By varying this parameter, there is a continuous transition from global effectiveness (all neurons contributing ) to local effectiveness (only first neighbors contributing) This topology has been studied in several contexts since the long-range interaction is observed in fundamental laws of physics, in coupled-oscillators networks [27], and in biological networks [5]. We see that different shuffles of the neuronal inputs may facilitate the sharing of information at the global level, leading to network PS They may hinder it, resulting in a PS degree smaller than observed in the uncoupled case.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
