Time-delay model of perceptual decision making in cortical networks
It is known that cortical networks operate on the edge of instability, in which oscillations can appear. However, the influence of this dynamic regime on performance in decision making, is not well understood. In this work, we propose a population model of decision making based on a winner-take-all mechanism. Using this model, we demonstrate that local slow inhibition within the competing neuronal populations can lead to Hopf bifurcation. At the edge of instability, the system exhibits ambiguity in the decision making, which can account for the perceptual switches observed in human experiments. We further validate this model with fMRI datasets from an experiment on semantic priming in perception of ambivalent (male versus female) faces. We demonstrate that the model can correctly predict the drop in the variance of the BOLD within the Superior Parietal Area and Inferior Parietal Area while watching ambiguous visual stimuli.\n\nAuthor summaryHuman cortex is a complex structure composed of thousands of tangled neural circuits. These circuits exhibit multiple modes of activity, depending on the local balance between excitatory and inhibitory activity. In particular, these circuits can exhibit oscillatory behavior, which is believed to be a manifestation of a so-called criticality: balancing on the edge between stable and unstable dynamics. Circuits in the cortex are responsible for higher cognitive functions such as, in example, perceptual decision making, i.e., evaluating properties of objects appearing in the visual field. However, it is not well known how aforementioned balancing on the edge of instability influences perceptual decision making.\n\nIn this work, we build a model to simulate dynamics of a very simple decision-making network consisting of two subpopulations. We then demonstrate that criticality in the network can account for ambiguity in decision making, and cause perceptual switches observed in human experiments. We further validate our model with datasets coming from a functional Magnetic Resonance Imaging experiment on semantic priming in perception of ambivalent (male versus female) faces. We demonstrate that the model can correctly predict the drop in the variance of the BOLD within the parietal areas of the cortex while watching ambiguous visual stimuli.