Gain Control in Predictive Smooth Pursuit Eye Movements: Evidence for an Acceleration-Based Predictive Mechanism.

The smooth pursuit eye movement system incorporates various control features enabling adaptation to specific tracking situations. In this work, we analyzed the interplay between two of these mechanisms: gain control and predictive pursuit. We tested human responses to high-frequency perturbations during step-ramp pursuit, as well as the pursuit of a periodically moving target. For the latter task, we found a nonlinear interaction between perturbation response and carrier acceleration. Responses to perturbations where the initial perturbation acceleration was contradirectional to carrier acceleration increased with carrier velocity, in a manner similar to that observed during step-ramp pursuit. In contrast, responses to perturbations with ipsidirectional initial perturbation and carrier acceleration were large for all carrier velocities. Modeling the pursuit system suggests that gain control and short-term prediction are separable elements. The observed effect may be explained by combining the standard gain control mechanism with a derivative-based short-term predictive mechanism. The nonlinear interaction between perturbation and carrier acceleration can be reproduced by assuming a signal saturation, which is acting on the derivative of the target velocity signal. Our results therefore argue for the existence of an internal estimate of target acceleration as a basis for a simple yet efficient short-term predictive mechanism.

Eye Velocity Gain Fields in MSTd During Optokinetic Stimulation.

Lesion studies argue for an involvement of cortical area dorsal medial superior temporal area (MSTd) in the control of optokinetic response (OKR) eye movements to planar visual stimulation. Neural recordings during OKR suggested that MSTd neurons directly encode stimulus velocity. On the other hand, studies using radial visual flow together with voluntary smooth pursuit eye movements showed that visual motion responses were modulated by eye movement-related signals. Here, we investigated neural responses in MSTd during continuous optokinetic stimulation using an information-theoretic approach for characterizing neural tuning with high resolution. We show that the majority of MSTd neurons exhibit gain-field-like tuning functions rather than directly encoding one variable. Neural responses showed a large diversity of tuning to combinations of retinal and extraretinal input. Eye velocity-related activity was observed prior to the actual eye movements, reflecting an efference copy. The observed tuning functions resembled those emerging in a network model trained to perform summation of 2 population-coded signals. Together, our findings support the hypothesis that MSTd implements the visuomotor transformation from retinal to head-centered stimulus velocity signals for the control of OKR.

Neuronal variability of MSTd neurons changes differentially with eye movement and visually related variables.

Neurons in macaque cortical area MSTd are driven by visual motion and eye movement related signals. This multimodal characteristic makes MSTd an ideal system for studying the dependence of neuronal activity on different variables. Here, we analyzed the temporal structure of spiking patterns during visual motion stimulation using 2 distinct behavioral paradigms: fixation (FIX) and optokinetic response. For the FIX condition, inter- and intra-trial variability of spiking activity decreased with increasing stimulus strength, complying with a recent neurophysiological study reporting stimulus-related decline of neuronal variability. In contrast, for the optokinetic condition variability increased together with increasing eye velocity while retinal image velocity remained low. Analysis of stimulus signal variability revealed a correlation between the normalized variance of image velocity and neuronal variability, but no correlation with normalized eye velocity variance. We further show that the observed difference in neuronal variability allows classifying spike trains according to the paradigm used, even when mean firing rates (FRs) were similar. The stimulus-dependence of neuronal variability may result from the local network structure and/or the variability characteristics of the input signals, but may also reflect additional timing-based mechanisms independent of the neuron's mean FR and related to the modality driving the neuron.

An information-theoretic approach for evaluating probabilistic tuning functions of single neurons.

Neuronal tuning functions can be expressed by the conditional probability of observing a spike given any combination of explanatory variables. However, accurately determining such probabilistic tuning functions from experimental data poses several challenges such as finding the right combination of explanatory variables and determining their proper neuronal latencies. Here we present a novel approach of estimating and evaluating such probabilistic tuning functions, which offers a solution for these problems. By maximizing the mutual information between the probability distributions of spike occurrence and the variables, their neuronal latency can be estimated, and the dependence of neuronal activity on different combinations of variables can be measured. This method was used to analyze neuronal activity in cortical area MSTd in terms of dependence on signals related to eye and retinal image movement. Comparison with conventional feature detection and regression analysis techniques shows that our method offers distinct advantages, if the dependence does not match the regression model.

The response of MSTd neurons to perturbations in target motion during ongoing smooth-pursuit eye movements.

Several regions of the brain are involved in smooth-pursuit eye movement (SPEM) control, including the cortical areas MST (medial superior temporal) and FEF (frontal eye field). It has been shown that the eye-movement responses to a brief perturbation of the visual target during ongoing pursuit increases with higher pursuit velocities. To further investigate the underlying neuronal mechanism of this nonlinear dynamic gain control and the contributions of different cortical areas to it, we recorded from MSTd (dorsal division of the MST area) neurons in behaving monkeys (Macaca mulatta) during step-ramp SPEM (5-20 degrees /s) with and without superimposed target perturbation (one cycle, 5 Hz, +/-10 degrees /s). Smooth-pursuit-related MSTd neurons started to increase their activity on average 127 ms after eye-movement onset. Target perturbation consistently led to larger eye-movement responses and decreasing latencies with increasing ramp velocities, as predicted by dynamic gain control. For 36% of the smooth-pursuit-related MSTd neurons the eye-movement perturbation was accompanied by detectable changes in neuronal activity with a latency of 102 ms, with respect to the eye-movement response. The remaining smooth-pursuit-related MSTd neurons (64%) did not reflect the eye-movement perturbation. For the large majority of cases this finding could be predicted by the dynamic properties of the step-ramp responses. Almost all these MSTd neurons had large visual receptive fields responding to motion in preferred directions opposite to the optimal SPEM stimulus. Based on these findings it is unlikely that MSTd plays a major role for dynamic gain control and initiation of the perturbation response. However, neurons in MSTd could still participate in SPEM maintenance. Due to their visual field properties they could also play a role in other functions such as self-motion perception.