title: On the formation of persistent states in neuronal network models of feature selectivity.
abstract: We study the existence and stability of localized activity states in neuronal network models of feature selectivity with either a ring or spherical topology. We find that the neural field has mono-stable, bi-stable, and tri-stable regimes depending on the parameters of the weighting function. In the case of homogeneous inputs, these localized activity states are marginally stable with respect to rotations. The response of a stable equilibrium to an inhomogeneous input is also determined.
authors.affiliation: Department of Mathematics, University of Utah, 155 S 1400 E JWB 233, Salt Lake City, Utah 84112, USA.
authors.forename: Evan C
authors.initials: EC
authors.lastname: Haskell
authors.forename: Paul C
authors.initials: PC
authors.lastname: Bressloff
mesh_terms: D000818:Animals; D006801:Humans; D016571:Neural Networks, Computer