title: Complex patterns arise through spontaneous symmetry breaking in dense homogeneous networks of neural oscillators.
abstract: There has been much interest in understanding collective dynamics in networks of brain regions due to their role in behavior and cognitive function. Here we show that a simple, homogeneous system of densely connected oscillators, representing the aggregate activity of local brain regions, can exhibit a rich variety of dynamical patterns emerging via spontaneous breaking of permutation or translational symmetries. Upon removing just a few connections, we observe a striking departure from the mean-field limit in terms of the collective dynamics, which implies that the sparsity of these networks may have very important consequences. Our results suggest that the origins of some of the complicated activity patterns seen in the brain may be understood even with simple connection topologies.
authors.affiliation: The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India.
authors.forename: Rajeev
authors.initials: R
authors.lastname: Singh
authors.affiliation: The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India.
authors.forename: Shakti N
authors.initials: SN
authors.lastname: Menon
authors.affiliation: The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India.|National Institute of Advanced Studies, Indian Institute of Science Campus, Bangalore 560012, India.
authors.forename: Sitabhra
authors.initials: S
authors.lastname: Sinha
mesh_terms: D000818:Animals; D001683:Biological Clocks; D001921:Brain; D008251:Macaca; D016571:Neural Networks, Computer; D009474:Neurons