Subspace mapping of the three-dimensional spectral receptive field of macaque MT neurons

Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions

Sensing the movement of fast objects within our visual environments is essential for controlling actions. It requires online estimation of motion direction and speed. We probed human speed representation using ocular tracking of stimuli of different statistics. First, we compared ocular responses to single drifting gratings (DGs) with a given set of spatiotemporal frequencies to broadband motion clouds (MCs) of matched mean frequencies. Motion energy distributions of gratings and clouds are point-like, and ellipses oriented along the constant speed axis, respectively. Sampling frequency space, MCs elicited stronger, less variable, and speed-tuned responses. DGs yielded weaker and more frequency-tuned responses. Second, we measured responses to patterns made of two or three components covering a range of orientations within Fourier space. Early tracking initiation of the patterns was best predicted by a linear combination of components before nonlinear interactions emerged to shape later dynamics. Inputs are supralinearly integrated along an iso-velocity line and sublinearly integrated away from it. A dynamical probabilistic model characterizes these interactions as an excitatory pooling along the iso-velocity line and inhibition along the orthogonal “scale” axis. Such crossed patterns of interaction would appropriately integrate or segment moving objects. This study supports the novel idea that speed estimation is better framed as a dynamic channel interaction organized along speed and scale axes.

Compound Stimuli Reveal the Structure of Visual Motion Selectivity in Macaque MT Neurons

Motion selectivity in primary visual cortex (V1) is approximately separable in orientation, spatial frequency, and temporal frequency (“frequency-separable”). Models for area MT neurons posit that their selectivity arises by combining direction-selective V1 afferents whose tuning is organized around a tilted plane in the frequency domain, specifying a particular direction and speed (“velocity-separable”). This construction explains “pattern direction-selective” MT neurons, which are velocity-selective but relatively invariant to spatial structure, including spatial frequency, texture and shape. We designed a set of experiments to distinguish frequency-separable and velocity-separable models and executed them with single-unit recordings in macaque V1 and MT. Surprisingly, when tested with single drifting gratings, most MT neurons’ responses are fit equally well by models with either form of separability. However, responses to plaids (sums of two moving gratings) tend to be better described as velocity-separable, especially for pattern neurons. We conclude that direction selectivity in MT is primarily computed by summing V1 afferents, but pattern-invariant velocity tuning for complex stimuli may arise from local, recurrent interactions.

Transfer function of the rhesus macaque oculomotor system for small-amplitude slow motion trajectories

Two main types of small eye movements occur during gaze fixation: microsaccades and slow ocular drifts. While microsaccade generation has been relatively well studied, ocular drift control mechanisms are unknown. Here we explored the degree to which monkey smooth eye movements, on the velocity scale of slow ocular drifts, can be generated systematically. Two male rhesus macaque monkeys tracked a spot moving sinusoidally, but slowly, along the horizontal or vertical direction. Maximum target displacement in the motion trajectory was 30 min arc (0.5°), and we varied the temporal frequency of target motion from 0.2 to 5 Hz. We obtained an oculomotor “transfer function” by measuring smooth eye velocity gain (relative to target velocity) as a function of frequency, similar to past work with large-amplitude pursuit. Monkey eye velocities as slow as those observed during slow ocular drifts were clearly target motion driven. Moreover, as with large-amplitude smooth pursuit, eye velocity gain varied with temporal frequency. However, unlike with large-amplitude pursuit, exhibiting low-pass behavior, small-amplitude motion tracking was band pass, with the best ocular movement gain occurring at ~0.8–1 Hz. When oblique directions were tested, we found that the horizontal component of pursuit gain was larger than the vertical component. Our results provide a catalog of the control abilities of the monkey oculomotor system for slow target motions, and they also support the notion that smooth fixational ocular drifts are controllable. This has implications for neural investigations of drift control and the image-motion consequences of drifts on visual coding in early visual areas.

NEW & NOTEWORTHY We studied the efficacy of monkey smooth pursuit eye movements for very slow target velocities. Pursuit was impaired for sinusoidal motions of frequency less than ~0.8–1 Hz. Nonetheless, eye trajectory was still sinusoidally modulated, even at velocities lower than those observed during gaze fixation with slow ocular drifts. Our results characterize the slow control capabilities of the monkey oculomotor system and provide a basis for future understanding of the neural mechanisms for slow ocular drifts.