A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning

Abstract

Classical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.