We used two 8-year-old, healthy male rhesus monkeys (Macaca mulatta) (monkey X and monkey D, respectively) that were obtained from the German Primate Centre (DPZ) in Göttingen and indoor housed in social groups. The monkeys were on a controlled feeding protocol during the training and recording period and their body weight was measured daily during that time. The daily amount of water was given as reward during, or if necessary after the sessions. Food was available ad libitum in the group cage. All experimental procedures were in accordance with the guidelines for animal experimentation determined by the responsible authority, the Regierungspräsidium Tübingen.

In previous iontophoretic experiments using exactly the same apparatus and methods, we have ensured that neuronal effects are not caused by positive ejection currents (). In such control experiments with 0.9% physiological NaCl and ejection currents of up to +40 nA (as used here), none of the tested neuronal responses, neither spontaneous activity nor any of the selective responses, were affected by ejection currents alone ().

Each recording day started with a control block followed by a drug block. On average, the length of control and drug blocks was 15 min, depending on the time the monkeys spent to do 108 correct trials. Retention (control phase) or ejection (drug phase) periods lasted for the full duration of a block, i.e., about 15 min. As iontophoretic drug application is fast and has been shown to modulate neuronal discharge rates quickly (), no data was excluded at the current switching points. Once the first drug block was finished, another control block followed by another drug block commenced. This sequence was repeated up to three times (i.e., three control and three drug blocks) until the monkeys were saturated and stopped working. Data from up to six successive blocks (or up to three control-drug successions, respectively) were analyzed, thus preventing systematic distortions of dopaminergic effects based on potential drifts of physiological conditions over time.

Electrode impedances were measured after the recordings and ranged between 0.5 and 3.5 MΩ (measured at 500 Hz; Omega Tip Z; World Precision Instruments). The pipette impedances typically ranged between 15-50 MΩ (full range: 10-350 MΩ) and were dependent on the opening diameter. As described previously () we used retention currents for all drugs of −7 nA. The D1 receptor (D1R) agonist SKF81297 (Sigma-Aldrich) was applied using ejection currents of +15 nA. For the D2 receptor (D2R) agonist quinpirole (Sigma-Aldrich) ejection currents of +40 nA were used. Both drugs were dissolved in ultra-pure, double distilled water at a concentration of 10 mM and a pH of 4.0 using HCl. For every recording day either SKF81297 or quinpirole was chosen and filled into one barrel per electrode. The other barrel was always filled with 0.9% NaCl to prevent excess drug solution to spill over into this barrel during the filling process. During the recording, iontophoresis conditions without (control) and with drug application (drug) alternated in blocks. Only one drug was tested per recording session.

Extracellular recordings and micro-iontophoretic drug application were performed as described previously (). On each session, up to three custom-made, tungsten-in-glass electrodes () with two flanking barrels were lowered transdurally into the brain using a modified electrical drive (NAN Drive). We randomly recorded single neurons and made no attempt to preselect task-selective neurons. Signal acquisition, filtering, amplification and digitalization were accomplished with the MAP system (Plexon). Waveform separation was performed offline (Offline Sorter; Plexon).

The surgery was performed while the monkeys were under general anesthesia. The animals were placed in a stereotaxic holder and implanted with titanium headposts and a recording chamber over the lateral PFC in the right hemisphere. The chamber was placed centrally over the posterior part of the principal sulcus, anterior to the sulcus arcuatus in both animals guided by landmarks obtained through MRI and stereotactic measurements, acquired prior to the surgeries. Recordings were performed equally in all available cortex without bias.

A trial was initiated by holding a metal bar and maintaining gaze on a central fixation target (fixation period). After 300 ms, a series of eight stimuli of pseudo-randomized motion directions was presented in sequence. At the end of each trial, a fluid reward was delivered if the monkey maintained holding the bar and maintained fixation throughout the stimulus presentation sequence. Each stimulus was presented for 300 ms and contained dot patterns moving in one of eight directions. Within one trial every possible direction was presented only once and the time between each stimulus was 100 ms. This passive fixation task in which the monkey only observed moving dot patterns (data of the current paper) was interleaved with trial blocks in which the monkey was actively engaged in a delayed response task (not reported here).

The monkeys were trained to watch a series of visual random dot patterns moving in the center of the screen. Stimuli were circular patches of random dots 5° of visual angle (dva) in diameter. The overall dot density was 12 per dva 2 with a radius of 0.04 dva for each dot and they moved with 100% coherence at a speed of 4 dva/s. The movement of the dots varied in eight directions, spaced evenly across the full 360°. The color of all dots was a light gray. All stimuli were generated using MATLAB (The MathWorks).

The experiment was conducted in a darkened operant conditioning chamber. The monkeys were seated in a primate chair in front of a computer screen that was used for the display of the visual stimuli. The animals used a metal bar to indicate their behavioral choices. Fluid reward was delivered by an automated reward system via a mouthpiece that was attached to the chair. CORTEX software (NIMH, Bethesda, MD) was used for experimental control and behavioral data acquisition. During each trial the monkey had to maintain eye fixation within 3.5° visual angle of the central fixation target (ISCAN). Neuronal data was recorded using a PLEXON system (Plexon Inc., Dallas, Texas).

Quantification and Statistical Analysis

All data analysis was performed using the R2018a release of MATLAB software. For neuronal population analysis, the data from both monkeys were pooled as they showed similar results. We used the same time window for all analysis and drugs as described in section ‘Direction-selective neurons’.

The following statistical tests were used as appropriate for the data: two factorial analysis of variance to define direction selective neurons. A chi-square test to test the frequency of direction selective neurons between monkeys. A Rayleigh test for circular uniformity of preferred directions of direction selective neurons. A Wilcoxon rank sum test to test for effects of drug application to the preferred or least-preferred direction on a single cell level. A paired Wilcoxon test for the same as the previous analysis on the level of the population of direction selective neurons. The paired Wilcoxon test was further used to test for the effects of drug application on the direction sensitivity (auROC), the Fano factor and differences in the goodness of fit on a population level. Changes in tuning width, as measured by Gaussian fit and full-width at half-height, were also tested using a paired Wilcoxon test. The influence of drug application on the decoding performance (SVM) was tested using a random permutation test.

Data were presented as mean ± standard error of the mean (SEM) unless indicated otherwise. p < 0.05 was considered to be statistically significant.

For all analyses the exact statistical test including the p values, dispersion and precision measures are given in the results section.

Direction-selective neurons All neurons recorded with at least 10 correct trials per motion direction in both the control and drug conditions were included in the analysis. A two factorial analysis of variance (ANOVA) with direction (eight levels: 0° to 315°) and iontophoresis condition (levels: control and drug) was performed on the discharge rates during stimulus presentation (0.18 s to 0.48 s after stimulus onset) on the pool of 296 recorded neurons. Neurons were counted as direction selective if they were significant for the main factor direction (p < 0.05). None of the reported results depended on the exact choice of time window for the analysis. Similar results were obtained using different parameters. All further analyses were performed on the pool of direction selective neurons as determined by the ANOVA (n = 82).

Single-cell and population responses For single-cell responses, spike-density functions were generated, i.e., individual trials were parsed in 10 ms bins and spikes convoluted with a Gaussian function with a width of sigma = 25 ms. Activity was then averaged for every 10 ms bin over all trials of a given direction.

Tuning curve fit Each direction-selective neuron was fit with a Gaussian function to analyze the individual tuning curve. For the fitting we used the four parameters, maximum response (a), preferred direction (b), width (c) and offset (d); see Equation 1 . MATLABs ‘nonlinearleastsquares’ algorithm was used to find optimal values for each neuron. For the example neurons in Figure 2 , raw discharge rates were used to compute the tuning curves. For the population tuning curves, we used baseline-corrected discharge rates to account for the influence of the drug on the neurons’ baseline level. To that aim, from all responses to the different directions in control and drug condition we subtracted the average baseline response separately for drug and control condition (e.g., responses in control minus average baseline in control). For each direction we calculated the error across trials as standard error of the mean (SEM). f ( x ) = a ∗ e − ( x − b c ) 2 + d Equation 1

To generate population tuning curves, all responses were aligned to the preferred direction and averaged over all neurons for each iontophoresis condition separately. The average response was then used to fit the population tuning curve using the same fitting procedure as for the example neurons. For each direction we calculated the error across neurons as standard error of the mean (SEM).

Tuning width analysis A second measure to describe the tuning width was the full-width at half-height of each neurons raw direction tuning profile. Like for the Gaussian fits, we used the baseline-corrected discharge rates to account for changes in the baseline level induced by the drug application. For all neurons we calculated the width of the tuning profile at 50% of their maximum response for control- and drug condition separately. Multi-Class Support Vector (SVM) classification Chang and Lin, 2011 Chang C.

Lin C. LIBSVM. To assess the directional information contained in the population of direction-selective neurons and to evaluate how this information changes with dopamine receptor stimulation, we trained a multi-class SVM classifier () (LIBSVM version 3.23). We used a linear SVM-kernel with default parameter settings and applied ‘one-versus-one’ classification to distinguish our eight directions. A separate classifier was built for each iontophoresis condition using 20 trials per neuron. For neurons with more than 20 trials we randomly sampled without replacement 20 trials. A small subset of 15% of our direction-selective neurons (13/82) was recorded with 10 to 19 trials. For these neurons we added the missing number of trials by randomly sampling with replacement from the pool of their trials. Thus, the SVM training and test datasets were completely distinct for 85% of the neurons; for the remaining 15% of the neurons, the training and test datasets mildly overlapped. We normalized all discharge rates by z-scoring and used leave-one-out cross-validation. In the confusion matrix the main diagonal contains correct labeling of the classifier. By averaging over the main and minor diagonals of the confusion matrix we calculated the decoding tuning curve. This process was repeated 1000 times and in each run the difference in average decoding performance between drug application and control (performance drug – performance control) was calculated. The application of the dopamine receptor agonists was considered to significantly increase the decoding performance, if less of than 5% of the distribution of differences was negative.

Receiver operating characteristic analysis To quantify the direction tuning selectivity, we used the receiver operating characteristic (ROC), which is a measure derived from the signal detection theory. For each direction selective neuron (n = 82) the ROC curve was generated by calculating the true positive rate (discharge rate in response to the preferred direction) and the false positive rate (discharge rate in response to the least-preferred direction) in each iontophoresis condition. Using the direction 180° opposite of the preferred direction as false positive yielded similar results. We then calculated the area und the ROC curve (auROC). The auROC is a nonparametric measure of the discriminability of two distributions and denotes the probability, by which an ideal observer is able to differentiate a meaningful signal from a noisy background. A perfect discrimination yields values of 1, while no separation is given by values of 0.5. As the auROC takes into account both the difference between distributions means and their width, it is a good indicator of signal quality.