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.
University of Utah
| Department of Mathematics
|
bressloff@math.utah.edu
Aug 2001.