Midbrain dopaminergic neurons are strongly implicated in Parkinson's disease, drug addiction, and reinforcement learning. Within reinforcement learning, they encode reward prediction error in their firing patterns. They fire bursts of action potentials in response to unexpected rewards or a conditioned stimulus that accurately predicts a future reward. Two plausible models that can account for reward-related responses are the disinhibition and two-signal, seesaw models. Both of these models require that NMDAmediated bursts can be suppressed by activation of post-synaptic GABAA or GABAB receptors. A combination of in vitro and in vivo experiments is proposed here to investigate GABAergic inhibition of bursting in midbrain dopaminergic neurons. NMDA-mediated bursts will be generated via local application of a NMDA agonist in vitro. Inhibition of these bursts by bath and locally applied GABAA or GABAB agonists will be investigated. Bursts will also be generated in vivo via local application of a NMDA agonist. GABAergic afferents will be electrically stimulated and inhibition of bursting by stimulation will be measured. Each proposed experiment also has a complementary computational component which is used to further explore the mechanisms of inhibition and generate predictions which are experimentally testable. GABAA and GABAB receptors will be added to a recent model of dopaminergic neuron bursting. The mechanisms of inhibition of NMDA-mediated bursting by activation of these receptors will be determined. Activation will first be modeled by stepwise changes in channel conductance. Next, we will model receptor activation according to the temporal pattern of synaptic inputs that a dopaminergic neuron receives as an integrated part of the basal ganglia network. Dopaminergic neurons were added to a recent model of the basal ganglia. Network activity evoked by GABAergic afferent activation will be recorded and GABAA or GABAB receptors will be activated according to this input. Inhibition of NMDA-mediated bursting by this network activity will be investigated.
By using a combination of in vitro and in vivo experiments complemented with computational studies, the role of GABAergic receptor activation in burst firing may be better understood. The proposed aims will provide insight into the role of GABA in reward prediction error studies and the afferent sources involved. The proposal would be strengthened by a better explanation of the experimental framework and underlying assumptions that guide the experiments.
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