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

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 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 corticostriatal 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 a computational model for 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. Results show that plasticity predominantly impacts direct pathway weights, which evolve to drive action selection toward a more-rewarded action in settings with deterministic reward outcomes. After the model is tuned based on such fixed reward scenarios, its performance agrees with the results of behavioral experiments carried out with probabilistic reward paradigms.