3.3 Electrophysiological Methods
3.3.6 Data Acquisition and Analysis
Online analysis of neural activity was accomplished with a C-based custom software.
Further data analyses and statistics were performed with scripts written in MATLAB (MathWorks, Natick, MA).
The average number of spikes a unit fired in a second of stimulus presentation was
computed as the firing rate. In experiment I, the firing rate was determined based on at least four repetitions (typically about eight) of identical stimulus presentations in different trials.
Speed tuning curves consisting of at least seven data points were then fit by a four parameter log-Gaussian with the formula:
f(x) = A+Bexp −1
where A denotes the baseline firing rate, B is the peak tuning amplitude,x0 repre-sents the speed at the peak position of the log-Gaussian, and logσ is the standard deviation of the log-Gaussian distribution.
The log-Gaussian, was fit with the Levenberg-Marquardt routine [35]. The peak of the fitted log-Gaussian was considered as the preferred speed for a given stimulus size. To test the changes in tuning width, the standard deviation of log-Gaussians were compared. The amplitude, which was equal to the difference between max-imum and minmax-imum response extracted from the log-Gaussian, was used as a measure of responsiveness and was compared for different sizes of stimuli as well.
A speed tuning index was computed for each speed tuning curve, where the amplitude (maximum - minimum) of the log-Gaussian was divided by the standard deviation. Units with poor speed tuning (i.e. units with too low or too high speed tuning indices) and visibly not well fit by the log-Gaussian curve were excluded from the analyses. Additionally, units with preferred speeds higher than 64◦/s were excluded from the data, since the stimulus speeds used did not exceed 64◦/s and it is not clear whether a unit with a higher preferred speed still can be considered as well fit with a log-Gaussian.
ROC curves in experiment II. For the analyses in the second experiment, re-ceiver operating characteristics (ROC) curves were computed. They have been widely used in signal detection theory [16] and medical diagnostics, as well as in choice probability analyses, seeking a correlation between neuronal activity and psychophysical data [6, 25]. In this study, ROC curves are used to measure the
separation between distributions of firing rates measured with different stimulus sizes. Therefore, this analysis serves as a statistical method for analyzing the simi-larity of the firing rates for differently sized stimuli. For this purpose, after mapping the receptive field and determining the preferred direction, the speed tuning curve was measured with a stimulus covering the receptive field, which was to be used as the largest stimulus in experiment II. After fitting speed tuning curves with log-Gaussians, the two steepest parts of each tuning curve were computed in the linear speed space: one on the ascending and the other on the descending arm, using an equation of the following form:
For clarity, the speeds corresponding to these two high slope points of the tun-ing curve are termed as the lower (for the ascendtun-ing arm) and the higher (for the descending arm) critical speed. One of these two speeds is taken as the constant stimulus speed of experiment II. The choice of the critical speed depended on the speed tuning profile and the overall responsiveness of the unit. In general, the lower critical speed was used for high-pass and band-pass units and the upper criti-cal speed was used for low-pass units. The division between high-pass and low-pass units was kept flexible and there was no strict criterion, since for some band-pass units with low firing rates, the lower critical speed evoked a very weak response.
Stimuli moving at this speed would elicit firing rates near zero. Therefore, although such units were not low-pass per se, the upper critical speed was used as well. After repeated presentations of random dot patterns in different trials, firing rates for three sizes of the stimuli were computed. For each stimulus size, the histograms of firing rates were computed with a bin size of 2.5 spikes/s. The frequencies in each bin were then summed cumulatively and the cumulative sums were plotted in pairs (i.e. stimulus covering 50% of the receptive field vs stimulus covering 100%
of the receptive field.) The resulting curve corresponded to the ROC curve and the area under this curve was computed for further analysis as described in results
of the experiment II.
Figure 3.1: Visual fixation task. The monkeys were trained to keep their gaze on the fixation square throughout the trial. Each trial consisted of presentations of random dot patterns (RDP) moving in two op-posite directions of the same motion axis. The figure depicts the upward direction as the preferred and the downward direction as the null direction. Each stimulus lasted for approximately 800 ms and moved at a speed randomly picked from a speed table (0.5, 1, 2, 4, 8, 16, 32 or 64◦/s).
3.4 Results
We recorded from a total number of 86 single units, 66 units were from monkey Z and 20 units were from monkey D. 59 of these units were used in the analyses of experiment I, and the remaining 27 were recorded for experiment 2. The majority of our sample consisted of single units with distinct and well isolated waveforms (∼89%).
Area MT is neighboured by area MST, which shows similar response properties to motion stimuli, such as direction and speed tuning [48]. However, area MST has a higher receptive field to eccentricity ratio [48]. To ensure that our single unit data is collected from area MT, we first analyzed the relationship between the classical receptive field size and receptive field eccentricity in the population of units we recorded from. The ratio of these two parameters in our sample was 0.99±0.05, indicating that recordings were in area MT. Another difference between these two areas is that MST units are tuned for complex motion such as expansion and contraction [42], whereas MT units are responsive but not necessarily tuned for such motion [12]. After isolating most of the motion responsive single units, we tested the responses to complex motion but observed no tuning in the spiral motion space, although most of the units displayed some nonselective response.
In the following sections, we summarize our results from two separate experi-ments. Experiment I investigates the stimulus size related changes in tuning pref-erences of MT units. Experiment II applies a signal detection theory quantifying the changes in firing rates of MT units, with regard to stimulus size. The data for these two experiments derive from different recordings, therefore, two different populations from the same two monkeys contribute to these results.