Corticostriatal synaptic weight evolution in a two-alternative forced choice task: a computational study

Abstract In natural environments, mammals can efficiently select actions based on noisy sensory signals and quickly adapt to unexpected outcomes to better exploit opportunities that arise in the future. Such feedback-based changes in behavior rely, in part, on long term plasticity within cortico-basal-ganglia-thalamic networks, driven by dopaminergic modulation of cortical inputs to the direct and indirect pathway neurons of the striatum. While the firing rates of striatal neurons have been shown to adapt across a range of feedback conditions, it remains difficult to directly assess the corticostriatal synaptic weight changes that contribute to these adaptive firing rates. In this work, we simulate the evolution of corticostriatal synaptic weights based on a spike timing-dependent plasticity rule driven by dopamine signaling that is induced by outcomes of actions in the context of a two-alternative forced choice task. Our results establish 1) that this plasticity model can successfully learn to select the most rewarding actions available, 2) that in the effective regime plasticity predominantly impacts direct pathway weights, evolving to drive action selection toward a more-rewarded action, and 3) that there can be coactivation of opposing populations within selected action channels, as observed experimentally. The model performance also agrees with the results of behavioral experiments carried out previously in human subjects using probabilistic reward paradigms.