title: Dynamic patterns in a two-dimensional neural field with refractoriness.
abstract: The formation of dynamic patterns such as localized propagating waves is a fascinating self-organizing phenomenon that happens in a wide range of spatially extended systems including neural systems, in which they might play important functional roles. Here we derive a type of two-dimensional neural-field model with refractoriness to study the formation mechanism of localized waves. After comparing this model with existing neural-field models, we show that it is able to generate a variety of localized patterns, including stationary bumps, localized waves rotating along a circular path, and localized waves with longer-range propagation. We construct explicit bump solutions for the two-dimensional neural field and conduct a linear stability analysis on how a stationary bump transitions to a propagating wave under different spatial eigenmode perturbations. The neural-field model is then partially solved in a comoving frame to obtain localized wave solutions, whose spatial profiles are in good agreement with those obtained from simulations. We demonstrate that when there are multiple such propagating waves, they exhibit rich propagation dynamics, including propagation along periodically oscillating and irregular trajectories; these propagation dynamics are quantitatively characterized. In addition, we show that these waves can have repulsive or merging collisions, depending on their collision angles and the refractoriness parameter. Due to its analytical tractability, the two-dimensional neural-field model provides a modeling framework for studying localized propagating waves and their interactions.
authors.affiliation: School of Physics, University of Sydney, New South Wales 2006, Australia.
authors.forename: Yang
authors.initials: Y
authors.lastname: Qi
authors.affiliation: School of Physics, University of Sydney, New South Wales 2006, Australia.
authors.forename: Pulin
authors.initials: P
authors.lastname: Gong
mesh_terms: D000200:Action Potentials; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics