Abstract We propose a new AdEx mean-field framework to model two networks of excitatory and inhibitory neurons, representing two cortical columns. The columns are interconnected with excitatory connections contacting both Regularly Spiking (excitatory) and Fast Spiking (inhibitory) cells. The model is biophysically plausible since it is based on intercolumnar excitation modeling the long range connections and intracolumnar excitation-inhibition modeling the short range connections. This configuration introduces a bicolumnar competition, sufficient for choosing between two alternatives. Each column represents a pool of neurons voting for one of the two alternatives indicated by two stimuli presented on a monitor in human and macaque experiments. We endow the model with a reward-driven learning mechanism which allows to capture the optimal strategy maximizing the cumulative reward, as well as to model the exploratory behavior of the participant. We compare the simulation results to the behavioral data obtained from the human and macaque experiments in terms of performance and reaction time. This model provides a biophysical ground for simpler phenomenological models proposed for similar decision-making tasks and can be applied to neurophysiological data. Finally, it can be embedded in whole-brain simulators, such as The Virtual Brain (TVB), to study decision-making in terms of large scale brain dynamics.

A biologically plausible decision-making model based on interacting cortical columns