Localized states in an unbounded neural field equation with smooth firing rate function: a multi-parameter analysis

Grégory Faye 1 James Rankin 1 Pascal Chossat 2
1 NEUROMATHCOMP
CRISAM - Inria Sophia Antipolis - Méditerranée , INRIA Rocquencourt, ENS Paris - École normale supérieure - Paris, UNS - Université Nice Sophia Antipolis, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : The existence of spatially localized solutions in neural networks is an important topic in neuroscience as these solutions are considered to characterize working (short-term) memory. We work with an unbounded neural network represented by the neural field equation with smooth firing rate function and a wizard hat spatial connectivity. Noting that stationary solutions of our neural field equation are equivalent to homoclinic orbits in a related fourth order ordinary differential equation, we apply normal form theory for a reversible Hopf bifurcation to prove the existence of localized solutions; further, we present results concerning their stability. Numerical continuation is used to compute branches of localized solution that exhibit snaking-type behaviour. We describe in terms of three parameters the exact regions for which localized solutions persist.
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Grégory Faye, James Rankin, Pascal Chossat. Localized states in an unbounded neural field equation with smooth firing rate function: a multi-parameter analysis. [Research Report] RR-7872, INRIA. 2012, pp.31. ⟨hal-00665464⟩

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