Abstract
Decision optimization is a crucial ability that allows animals to interact with and to adapt to changing environments. Although many decision theories have been proposed to explain the process of finding best choices or the optimal decision, we are still lack of a detailed neural network model that explains how the nervous systems achieve optimization with preference given to speed or accuracy, and how the systems adapt to changes in the environment. The present study addresses the questions by proposing an integrated model that combines dopamine-modulated synaptic plasticity and a cotico-basal ganglia circuit model for perceptual decisions. In the integrated model, the cortical module detects signals and accumulates evidence, while the basal ganglia module acts as a threshold detector by disinhibiting the superior colliculus when the upstream cortical signal exceeds a certain level. Moreover, the synaptic strength between the cortex and striatum determines the decision threshold and is modulated by reward information through the release of dopamine. Our model shows that decision optimization and adaptation could be achieved via the interaction between the dopamine system and the cotico-basal ganglia circuit. In addition, the tendency to make fast or accurate decision can be explained in our model by dynamic balancing between the facilitating and depressing components of the dopamine dynamics. Specifically, the circuit model favors speed if we increase the phasic dopamine response to the reward prediction error, whereas the model favors accuracy if we reduce the tonic dopamine activity or the phasic dopamine responses to the estimated reward probability. The proposed model provides insight into the roles of different components of dopamine responses in decision adaptation and optimization in a changing environment.