Correlated variability of neuronal responses is an important factor in estimating sensory parameters from a population response. Large correlations among neurons reduce the effective size of a neural population and increase the variation of the estimates. They also allow the activity of one neuron to be informative about impending perceptual decisions or motor actions on single trials. In extrastriate visual area MT of the rhesus macaque, for example, some but not all neurons show nonzero "choice probabilities" for perceptual decisions or non-zero "MT-pursuit" correlations between the trial-by-trial variations in neural activity and smooth pursuit eye movements. To understand the functional implications of zero versus nonzero correlations between neural responses and impending perceptions or actions, we took advantage of prior observations that specific frequencies of local field potentials reflect the correlated activity of neurons. We found that the strength of the spike-field coherence of a neuron in the gamma-band frequency range is related to the size of its MT-pursuit correlations for eye direction, as well as to the size of the neuron-neuron correlations. Spike-field coherence predicts MT-pursuit correlations better for direction than for speed, perhaps because the topographic organization of direction preference in MT is more amenable to creating meaningful local field potentials. We suggest that the relationship between spiking and local-field potentials is stronger for neurons that have larger correlations with their neighbors; larger neuron-neuron correlations create stronger MT-pursuit correlations. Neurons that lack strong correlations with their neighbors also have weaker correlations with pursuit behavior, but still could drive pursuit strongly.

Sensory-motor behavior results from a complex interaction of noisy sensory data with priors based on recent experience. By varying the stimulus form and contrast for the initiation of smooth pursuit eye movements in monkeys, we show that visual motion inputs compete with two independent priors: one prior biases eye speed toward zero; the other prior attracts eye direction according to the past several days' history of target directions. The priors bias the speed and direction of the initiation of pursuit for the weak sensory data provided by the motion of a low-contrast sine wave grating. However, the priors have relatively little effect on pursuit speed and direction when the visual stimulus arises from the coherent motion of a high-contrast patch of dots. For any given stimulus form, the mean and variance of eye speed covary in the initiation of pursuit, as expected for signal-dependent noise. This relationship suggests that pursuit implements a trade-off between movement accuracy and variation, reducing both when the sensory signals are noisy. The tradeoff is implemented as a competition of sensory data and priors that follows the rules of Bayesian estimation. Computer simulations show that the priors can be understood as direction-specific control of the strength of visual-motor transmission, and can be implemented in a neural-network model that makes testable predictions about the population response in the smooth eye movement region of the frontal eye fields.

Sensory inputs control motor behavior with a strength, or gain, that can be modulated according to the movement conditions. In smooth pursuit eye movements, the response to a brief perturbation of target motion is larger during pursuit of a moving target than during fixation of a stationary target. As a step toward identifying the locus and mechanism of gain modulation, we test whether it acts on signals that are in visual or motor coordinates. Monkeys tracked targets that moved at 15°/s in one of eight directions, including left, right, up, down, and the four oblique directions. In eight-ninths of the trials, the target underwent a brief perturbation that consisted of a single cycle of a 10 Hz sine wave of amplitude ±5°/s in one of the same eight directions. Even for oblique directions of baseline target motion, the magnitude of the eye velocity response to the perturbation was largest for a perturbation near the axis of target motion and smallest for a perturbation along the orthogonal axis. Computational modeling reveals that our data are reproduced when the strength of visual-motor transmission is modulated in sensory coordinates, and there is a static motor bias that favors horizontal eye movements. A network model shows how the output from the smooth eye movement region of the frontal eye fields (FEF(SEM)) could implement gain control by shifting the peak of a visual population response along the axes of preferred image speed and direction.