The outstanding question of whether these changes are due to differential inputs or due to intrinsic properties of the neurons remains unanswered, as does the extent to which these mechanisms are involved in experience-dependent learning such as drug seeking. It will be important to measure plasticity in response to more behaviorally relevant protocols that emulate learning in response to reward and aversion, perhaps incorporating optogenetic or other approaches

to more clearly isolate particular inputs altered by stimuli. Further characterization of changes in the AMPA to NMDA ratio in Ih− DA neurons is required to determine if these cells exhibit a change in the subunit composition of AMPA receptors (e.g., a switch to calcium-permeable GluA2-lacking receptors) that has been linked to drug-induced Epacadostat in vitro behavioral sensitization and conditioned place preference (Lüscher and Malenka, 2011). A related issue is that while altered DA neurotransmission in the striatum and NAc is strongly implicated http://www.selleckchem.com/products/pd-0332991-palbociclib-isethionate.html in various aspects of drug dependence, it is less clear if an altered AMPA to NMDA ratio as a form of plasticity plays a role. If the AMPA receptors are maximally induced by exposure to an addictive drug, would this occlude reward-related

learning for the duration? It may be that the more complex alterations at corticostriatal synapses induced by these drugs lead to very long-term habits. The finding that a drug that elevates DA transmission and is associated with reward or addiction (and pain, as a model of aversive stimuli) could involve analogous synaptic plasticity at different DA cells certainly will motivate new investigations. The excitatory input to the DA cells is extensive and involves glutamatergic afferents from the prefrontal cortex, superior colliculus, pedunculopontine tegmental nucleus, lateral dorsal tegmental nucleus, subthalamic nucleus, and additional areas (Sesack and Grace, SB-3CT 2010), and any of these could be responsible for differential responses of the VTA neurons. Moreover, there are multiple

inhibitory and modulatory inputs and collaterals, and appropriate disinhibition or frequency-dependent filtering could play the key role in determining which inputs mediate this diverse plasticity. Using anatomically rigorous techniques, Lammel et al. have now provided us a far more detailed roadmap of the VTA. Future studies of these neurons will need to take into account more precisely which DA neurons are examined, including whether a neuron expresses TH+ and expresses Ih, with some idea of where the projections lie. As it is now relatively clear that some VTA DA neurons use glutamate as a cotransmitter (Hnasko et al., 2010), precisely which of them do so, and why? Most promisingly, these findings suggest new means to determine more precisely which synapses regulate behavior.