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Changes of Mind in an Attractor Network of Decision-Making
 

Summary: Changes of Mind in an Attractor Network of
Decision-Making
Larissa Albantakis1
*, Gustavo Deco1,2
1 Department of Information and Communication Technologies, Computational Neuroscience, Universitat Pompeu Fabra, Barcelona, Spain, 2 Institucio´ Catalana de la
Recerca i Estudis Avanc¸ats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain
Abstract
Attractor networks successfully account for psychophysical and neurophysiological data in various decision-making tasks.
Especially their ability to model persistent activity, a property of many neurons involved in decision-making, distinguishes
them from other approaches. Stable decision attractors are, however, counterintuitive to changes of mind. Here we
demonstrate that a biophysically-realistic attractor network with spiking neurons, in its itinerant transients towards the
choice attractors, can replicate changes of mind observed recently during a two-alternative random-dot motion (RDM) task.
Based on the assumption that the brain continues to evaluate available evidence after the initiation of a decision, the
network predicts neural activity during changes of mind and accurately simulates reaction times, performance and
percentage of changes dependent on difficulty. Moreover, the model suggests a low decision threshold and high incoming
activity that drives the brain region involved in the decision-making process into a dynamical regime close to a bifurcation,
which up to now lacked evidence for physiological relevance. Thereby, we further affirmed the general conformance of
attractor networks with higher level neural processes and offer experimental predictions to distinguish nonlinear attractor
from linear diffusion models.
Citation: Albantakis L, Deco G (2011) Changes of Mind in an Attractor Network of Decision-Making. PLoS Comput Biol 7(6): e1002086. doi:10.1371/

  

Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra

 

Collections: Computer Technologies and Information Sciences