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1 Neural Science, New York University, New York, New York, United States
2 Center for Neurobiology and Behavior, Columbia University, New York, New York, United States; Center for Neural Science, New York University, New York, New York, United States
3 Center for Neural Science, New York University, New York, New York, United States
* To whom correspondence should be addressed. E-mail: glimcher{at}cns.nyu.edu.
Work in behaving primates indicates that midbrain dopamine neurons encode a prediction error, the difference between an obtained reward and the reward expected. Studies of dopamine action potential timing in the alert and anaesthetized rat indicate that dopamine neurons respond in tonic and phasic modes, a distinction that has been less well characterized in the primates. We used spike train models to examine the relationship between the tonic and burst modes of activity in dopamine neurons while monkeys were performing a reinforced visuo-saccadic movement task. We studied spiking activity during four task-related intervals; two of these were intervals during which no task-related events occurred, while two were periods marked by task-related phasic activity. We found that dopamine neuron spike trains during the intervals when no events occurred were well described as tonic. Action potentials appeared to be independent, to occur at low frequency, and to be almost equally well described by Gaussian and Poisson-like (Gamma) processes. Unlike in the rat, interspike intervals as low as 20 ms were often observed during these presumptively tonic epochs. Having identified these periods of presumptively tonic activity we were able to quantitatively define phasic modulations (both increases and decreases in activity) during the intervals in which task-related events occurred. This analysis revealed that the phasic modulations of these neurons include both bursting, as has been described previously, and pausing. Together bursts and pauses seemed to provide a continuous, although non-linear, representation of the theoretically defined reward prediction error of reinforcement learning.
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