title: Dynamics of a neural system with a multiscale architecture.
abstract: The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales-neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are 'slaved' to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested.
authors.affiliation: The Black Dog Institute, Prince of Wales Hospital and School of Psychiatry, University of New South Wales, Randwick, NSW 2031, Australia. mbreak@physics.usyd.edu.au
authors.forename: Michael
authors.initials: M
authors.lastname: Breakspear
authors.forename: Cornelis J
authors.initials: CJ
authors.lastname: Stam
mesh_terms: D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D009991:Oscillometry / Q000379:methods; D013997:Time Factors