Reward-biased probabilistic decision-making: Mean-field predictions and spiking simulations

In this work we study the basic competitive and cooperative mechanisms of neural activity in the context of a two-alternative free-choice eye-movement task, as a function of the expectation of reward. We use a simplified version of the protocol followed by Platt and Glimcher [Neural correlates of decision variables in parietal cortex, Nature 400 (1999) 233–238], in which each choice is associated with independent underlying reward schedules, and explicitly model it using a biophysically realistic network of integrate-and-fire neurons that forms a categorical choice from the expected gain contingencies, via a simple bias mechanism. The model accounts for several experimental findings, such as the gain-modulated firing activity observed by Platt and Glimcher and the matching law.