@article {47036,
	title = {Dopamine is required for the neural representation and control of movement vigor.},
	journal = {Cell},
	volume = {162},
	year = {2015},
	month = {2015 Sep 10},
	pages = {1418-30},
	abstract = {<p>Progressive depletion of midbrain dopamine neurons (PDD) is associated with deficits in the initiation, speed, and fluidity of voluntary movement. Models of\&nbsp;basal ganglia function focus on initiation deficits; however, it is unclear how they account for deficits in the speed or amplitude of movement (vigor). Using an effort-based operant conditioning task for head-fixed mice, we discovered distinct functional classes of neurons in the dorsal striatum that represent movement vigor. Mice with PDD exhibited a progressive reduction in vigor, along with a selective impairment of its neural representation in striatum. Restoration of dopaminergic tone with a synthetic precursor ameliorated deficits in movement vigor and its neural representation, while suppression of striatal activity during movement was sufficient to reduce vigor. Thus, dopaminergic input to the dorsal striatum is indispensable for the emergence of striatal activity that mediates adaptive changes in movement vigor. These results suggest refined intervention strategies for Parkinson{\textquoteright}s disease.</p>
},
	doi = {10.1016/j.cell.2015.08.014},
	author = {Panigrahi, Babita and Martin, Kathleen A and Li, Yi and Graves, Austin R and Vollmer, Alison and Olson, Lars and Mensh, Brett D and Karpova, Alla Y and Dudman, Joshua T}
}
@article {6485,
	title = {Mice infer probabilistic models for timing.},
	journal = {Proceedings of the National Academy of Sciences of the United States of America},
	volume = {110},
	year = {2013},
	month = {2013 Sep 30},
	pages = {17154-9},
	abstract = {<p>Animals learn both whether and when a reward will occur. Neural models of timing posit that animals learn the mean time until reward perturbed by a fixed relative uncertainty. Nonetheless, animals can learn to perform actions for reward even in highly variable natural environments. Optimal inference in the presence of variable information requires probabilistic models, yet it is unclear whether animals can infer such models for reward timing. Here, we develop a behavioral paradigm in which optimal performance required knowledge of the distribution from which reward delays were chosen. We found that mice were able to accurately adjust their behavior to the SD of the reward delay distribution. Importantly, mice were able to flexibly adjust the amount of prior information used for inference according to the moment-by-moment demands of the task. The ability to infer probabilistic models for timing may allow mice to adapt to complex and dynamic natural environments.</p>
},
	issn = {1091-6490},
	doi = {10.1073/pnas.1310666110},
	author = {Li, Yi and Dudman, Joshua Tate}
}
