Robust persistent activity in neural fields with asymmetric connectivity

Abstract

Modeling studies have shown that recurrent interactions within neural networks are capable of self-sustaining non-uniform activity profiles. These patterns are thought to be the neural basis of working memory. However, the lack of robustness challenge this view as already small deviations from the assumed interaction symmetry destroy the attractor state. Here we analyze attractor states of a neural field model composed of bistable neurons. We show the existence of self-stabilized patterns that robustly represent the cue position in the presence of a substantial asymmetry in the connection profile. Using approximation techniques we derive an explicit expression for a threshold value describing the transition to a traveling activity wave.

Can self-organisation emerge through dynamic neural fields computation?

“…Another computational feature is the use of traveling pulses, whose stability has also been studied [23]. Some authors consider such waves as produced by cortical areas in abnormal conditions, as hallucinations [6], but traveling waves can also be obtained by breaking the symmetry of the lateral coupling weight kernel.…”