Striatal dopamine ramping may indicate flexible reinforcement learning with forgetting in the cortico-basal ganglia circuits

It has been suggested that the midbrain dopamine (DA) neurons, receiving inputs from the cortico-basal ganglia (CBG) circuits and the brainstem, compute reward prediction error (RPE), the difference between reward obtained or expected to be obtained and reward that had been expected to be obtained. These reward expectations are suggested to be stored in the CBG synapses and updated according to RPE through synaptic plasticity, which is induced by released DA. These together constitute the “DA=RPE” hypothesis, which describes the mutual interaction between DA and the CBG circuits and serves as the primary working hypothesis in studying reward learning and value-based decision-making. However, recent work has revealed a new type of DA signal that appears not to represent RPE. Specifically, it has been found in a reward-associated maze task that striatal DA concentration primarily shows a gradual increase toward the goal. We explored whether such ramping DA could be explained by extending the “DA=RPE” hypothesis by taking into account biological properties of the CBG circuits. In particular, we examined effects of possible time-dependent decay of DA-dependent plastic changes of synaptic strengths by incorporating decay of learned values into the RPE-based reinforcement learning model and simulating reward learning tasks. We then found that incorporation of such a decay dramatically changes the model's behavior, causing gradual ramping of RPE. Moreover, we further incorporated magnitude-dependence of the rate of decay, which could potentially be in accord with some past observations, and found that near-sigmoidal ramping of RPE, resembling the observed DA ramping, could then occur. Given that synaptic decay can be useful for flexibly reversing and updating the learned reward associations, especially in case the baseline DA is low and encoding of negative RPE by DA is limited, the observed DA ramping would be indicative of the operation of such flexible reward learning.