Dynamic gain control of dopamine delivery in freely moving animals

Abstract

Activity changes in a large subset of midbrain dopamine neurons fulfill numerous assumptions of learning theory by encoding a prediction error between actual and predicted reward. This computational interpretation of dopaminergic spike activity invites the important question of how changes in spike rate are translated into changes in dopamine delivery at target neural structures. Using electrochemical detection of rapid dopamine release in the striatum of freely moving rats, we established that a single dynamic model can capture all the measured fluctuations in dopamine delivery. This model revealed three independent short-term adaptive processes acting to control dopamine release. These short-term components generalized well across animals and stimulation patterns and were preserved under anesthesia. The model has implications for the dynamic filtering interposed between changes in spike production and forebrain dopamine release.

Figure 1.

Extracellular dopamine in the caudate-putamen of freely moving rats. A, A dopamine transient evoked by a repetitive electrical stimulation (24 pulses, 60 Hz, 120 μA; horizontal bar) delivered to dopamine neurons in the substantia nigra/ventral tegmental area with a bipolar electrode (left panel). The amplitude and duration of this concentration transient are quite similar to a subsecond dopamine transient elicited in another male rat after exposure to an estrous female (right panel). B, Dopamine concentration transients evoked by an irregular stimulation train where each stimulus (24 pulses; vertical bar) is a repetitive electrical stimulation identical to that delivered in A. The irregular stimulus pattern is a “playback” of an intracranial self-stimulation lever-press pattern of another rat. Both facilitation (compare amplitudes at 1 and 2) and depression (3) in the dynamics governing evoked release are apparent.

Figure 2.

Dynamic model reveals multiple adaptive mechanisms for ongoing dopamine release. A, Semi-log plot of the moralized error for fitting the model to experimental data versus the number of independent dynamic components I_j. Representative fits are shown from four representative animals. The error decreases up to three components and does not improve appreciably beyond that. B, The three dynamic components consistently captured by the model: short-lasting facilitation (top), short-lasting depression (middle), and longer-lasting depression (bottom). These time-dependent mathematical components are induced by each action potential (arrow) and multiplicatively modify the amplitude of dopamine release for future action potentials. C, Fit of dynamic model to dopamine released during a single repetitive electrical stimulation (24 pulses, 60 Hz, 120 μA) applied to dopamine neurons of an ambulant rat. In all panels, the model is magenta, data are blue, and r² is the correlation coefficient of the goodness of fit. D, Fit of model to dopamine fluctuations evoked in an anesthetized animal by more intense stimulus trains. Each repetitive stimulus (vertical bar) consisted of 600 pulses (60 Hz, 120 μA). The repetitive stimulus trains were delivered at 2, 5, 10, or 20 min intervals. Depression is present that is still evident on the next stimulus with 2 min interstimulus intervals. At longer intervals there is greater recovery of release. Inset, Response for a single 600 pulse train (60 Hz, 120 μA). E, Fit of model to dopamine fluctuations evoked by stimuli as in C repeated at 2 sec intervals. Facilitation is apparent. F, Stimuli as in C repeated at 5 sec intervals. A gradual depression is apparent.

Figure 3.

Dopamine release for complex stimulus patterns. A, Best fit of model to dopamine fluctuations evoked by repetitive stimulus trains (24 pulses, 60 Hz) spaced regularly at 1 sec intervals. Facilitation is apparent. The model is magenta, data are blue, and r² is the correlation coefficient of the goodness of fit. B, The parameters extracted from the fit to the data in A (in one animal) were used to predict dopamine fluctuations evoked by the repetitive stimulus delivered (in another animal) at irregular intervals at approximately the same rate (∼1 Hz). The prediction is magenta, data are blue, and r² is the correlation coefficient of the goodness of fit. A more complete exploration of the parameter space for the data shown in B will improve the fit, but not dramatically indicating good generalization across animals. C, Another irregular pattern was fit by the model (model = magenta; measured dopamine = black). D, Plot of short-term depression versus facilitation time constants shows a consistent clustering. Each dot represents a fit to the measured dopamine fluctuations in a single animal. The red and green dots indicate the time constants used for the fits from A-C.