title: Nonlinear dynamics of direction-selective recurrent neural media.
abstract: The direction selectivity of cortical neurons can be accounted for by asymmetric lateral connections. Such lateral connectivity leads to a network dynamics with characteristic properties that can be exploited for distinguishing in neurophysiological experiments this mechanism for direction selectivity from other possible mechanisms. We present a mathematical analysis for a class of direction-selective neural models with asymmetric lateral connections. Contrasting with earlier theoretical studies that have analyzed approximations of the network dynamics by neglecting nonlinearities using methods from linear systems theory, we study the network dynamics with nonlinearity taken into consideration. We show that asymmetrically coupled networks can stabilize stimulus-locked traveling pulse solutions that are appropriate for the modeling of the responses of direction-selective neurons. In addition, our analysis shows that outside a certain regime of stimulus speeds the stability of these solutions breaks down, giving rise to lurching activity waves with specific spatiotemporal periodicity. These solutions, and the bifurcation by which they arise, cannot be easily accounted for by classical models for direction selectivity.
authors.affiliation: Department of Brain and Cognitive Sciences and Center for Biological and Computational Learning, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. xhx@ai.mit.edu
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Xie
authors.forename: Martin A
authors.initials: MA
authors.lastname: Giese
mesh_terms: D000818:Animals; D001703:Biophysics / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D008962:Models, Theoretical; D009474:Neurons / Q000502:physiology*; D009482:Neurophysiology / Q000379:methods*; D013997:Time Factors