AbstractDopamine (DA) neurons in the ventral tegmental area (VTA) are thought to encode reward prediction errors (RPE) by comparing actual and expected rewards. In recent years, much work has been done to identify how the brain uses and computes this signal.While several lines of evidence suggest the the interplay of he DA and the inhibitory interneurons in the VTA implements the RPE computaiton, it still remains unclear how the DA neurons learn key quantities, for example the amplitude and the timing of primary rewards during conditioning tasks. Furthermore, exogenous nicotine and endogenous acetylcholine, acting on both VTA DA and GABA (γ - aminobutyric acid) neurons via nicotinic-acetylcholine receptors (nAChRs), also likely affect these computations. To explore the potential circuit-level mechanisms for RPE computations during classical-conditioning tasks, we developed a minimal computational model of the VTA circuitry. The model was designed to account for several reward-related properties of VTA afferents and recent findings on VTA GABA neuron dynamics during conditioning.With our minimal model, we showed that the RPE can be learned by a two-speed process computing reward timing and magnitude. Including a model of nAChR-mediated currents in the VTA DA-GABA circuit, we also showed that nicotine should reduce the acetylcholine action on the VTA GABA neurons by receptor desensitization and therefore potentially boost the DA responses to reward information. Together, our results delineate the mechanisms by which RPE are computed in the brain, and suggest a hypothesis on nicotine-mediated effects on reward-related perception and decision-making.
Mefloquine effects on ventral tegmental area dopamine and GABA neuron inhibition: A physiologic role for connexin-36 GAP junctions
