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ON THE FORMATION OF PERSISTENT STATES IN NEURONAL NETWORK MODELS OF FEATURE SELECTIVITY

EVAN C. HASKELL

Department of Mathematics, University of Utah, 155 S 1400 E JWB 233, Salt Lake City, Utah 84112, USA

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and

PAUL C. BRESSLOFF

Department of Mathematics, University of Utah, 155 S 1400 E JWB 233, Salt Lake City, Utah 84112, USA

Corresponding author.

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https://doi.org/10.1142/S0219635203000202Cited by:3 (Source: Crossref)

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.

Keywords:

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