Changes of mind in an attractor network of decision-making

Abstract

Attractor networks successfully account for psychophysical and neurophysiological data in various decision-making tasks./nEspecially their ability to model persistent activity, a property of many neurons involved in decision-making, distinguishes/nthem from other approaches. Stable decision attractors are, however, counterintuitive to changes of mind. Here we/ndemonstrate that a biophysically-realistic attractor network with spiking neurons, in its itinerant transients towards the/nchoice attractors, can replicate changes of mind observed recently during a two-alternative random-dot motion (RDM) task./nBased on the assumption that the brain continues to evaluate available evidence after the initiation of a decision, the/nnetwork predicts neural activity during changes of mind and accurately simulates reaction times, performance and/npercentage of changes dependent on difficulty. Moreover, the model suggests a low decision threshold and high incoming/nactivity that drives the brain region involved in the decision-making process into a dynamical regime close to a bifurcation,/nwhich up to now lacked evidence for physiological relevance. Thereby, we further affirmed the general conformance of/nattractor networks with higher level neural processes and offer experimental predictions to distinguish nonlinear attractor/nfrom linear diffusion models.