For each monkey (Figure 3F), the size of learning across our sample of FEFSEM neurons showed the highest correlation with the neural preference near 250 ms, the time of instruction, and lower correlations with neural preference at earlier or later times. Thus, learning with an instruction time of 250 ms engages neurons that specifically prefer 250 ms. The temporally-selective

relationship between neural preference and the magnitude of neural learning in Figure 3F provides evidence that the distributed representation of time within the FEFSEM may be used to regulate the temporal specificity of selleckchem pursuit learning. As an alternate way to examine the relationship between the amount of neural learning in an FEFSEM neuron and its temporal preference during pursuit, we plotted the magnitude of neural learning as a function of the difference between the neuron’s preferred time and 250 ms (Figure 3D). There

is considerable scatter in the plot, but for the population as a whole learning is largest in neurons with preferred times close to 250 ms, and is smaller in neurons with earlier or later preferred times. A small subpopulation of neurons exhibited negative learned responses, but the preferred times of these neurons were evenly distributed before and after the instruction time. The size of neural learning also was positively this website correlated with the size of the learned eye velocity and the opponent response of the neuron, defined as the difference in mean firing rate between prelearning pursuit in the probe direction versus in the learning direction, measured in the Metalloexopeptidase interval from 100 to 320 ms after the onset of target motion. Partial correlation analysis (Table 1) revealed that a strong correlation between

the magnitude of neural learning and the neural preference for 250 ms persisted even when the correlations with the other variables were taken into account. The size of the opponent response during prelearning pursuit was not a statistically significant predictor of the magnitude of learning. Not surprisingly, the magnitude of the learned eye velocity was a strong predictor of the magnitude of neural learning in Monkey S, who had wider variation in the size of his behavioral learning. We now ask whether the magnitude of neural learning varies systematically within an individual neuron when we alter the instruction time. The same neuron was exposed to two learning experiments featuring different instruction times associated with disparate neural preferences. The results in Figure 3 predict that the example neuron in Figure 4A should show larger learning for an instruction time of 150 ms, when its neural preference was 1.