On the formation of persistent states in neuronal network models of feature selectivity.
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.
Haskell
Evan C
EC
Department of Mathematics, University of Utah, 155 S 1400 E JWB 233, Salt Lake City, Utah 84112, USA.
Bressloff
Paul C
PC
D000818:Animals; D006801:Humans; D016571:Neural Networks, Computer