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

We present a new AdEx mean-field framework to model two networks of excitatory and inhibitory neurons, representing two cortical columns, and interconnected with excitatory connections contacting both Regularly Spiking (excitatory) and Fast Spiking (inhibitory) cells. This connection scheme is biophysically plausible since it is based on intercolumnar excitation and intracolumnar excitation-inhibition. This configuration introduces bicolumnar competition, sufficient for choosing between two alternatives. Each column represents a pool of neurons voting for one of two choices indicated by two stimuli presented on a monitor in human and macaque experiments. The task also requires maximizing the cumulative reward over each episode, which consists of a certain number of trials. The cumulative reward depends on the coherency between choices of the participant/model and preset strategy in the experiment. We endow the model with a reward-driven learning mechanism allowing to capture the implemented strategy, as well as to model individual exploratory behavior. 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 obtained from the macaque brain. 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. ### Competing Interest Statement The authors have declared no competing interest.