Spatio-temporal requirements for direction selectivity in area 18 and PMLS complex cells

The spatio-temporal requirements for direction selectivity were studied in two extrastriate motion processing areas in the cat, area 18 and the posteromedial lateral suprasylvian cortex (PMLS). Direction, velocity and pixel size of random pixel arrays (RPA) were adjusted for each neuron and direction selectivity was measured as a function of step size and delay for a given optimal velocity. A subset of direction selective complex cells in area 18 was tuned to intermediate step size and delay combinations rather than the smoothest motion (band-pass cells). Other area 18 complex cells responded best to the smallest value of step size and delay (low-pass cells). Tuning varied with the pixel size of the RPA. Cells with tuning for smaller pixels favoured a preference for non-smooth motion. Area 18 cells with lower spatial resolution showed larger optimal and maximal step sizes. For a subset of the cells in area 18, we measured direction selectivity for extensive step-delay combinations, covering multiple velocities. Results showed that most cells were tuned to narrow range of step-delay combinations, and that the optimal step size was independent of temporal delay. Direction selective complex cells in PMLS were tuned to larger pixel sizes than those in area 18, although the distributions did overlap. In contrast to area 18, PMLS cells preferred the smoothest motion, irrespective of RPA pixel size.</p

Aftereffect of high-speed motion

A visual illusion known as the motion aftereffect is considered to be the perceptual manifestation of motion sensors that are recovering from adaptation. This aftereffect can be obtained for a specific range of adaptation speeds with its magnitude generally peaking for speeds around 3 deg s-1. The classic motion aftereffect is usually measured with a static test pattern. Here, we measured the magnitude of the motion aftereffect for a large range of velocities covering also higher speeds, using both static and dynamic test patterns. The results suggest that at least two (sub)populations of motion-sensitive neurons underlie these motion aftereffects. One population shows itself under static test conditions and is dominant for low adaptation speeds, and the other is prevalent under dynamic test conditions after adaptation to high speeds. The dynamic motion aftereffect can be perceived for adaptation speeds up to three times as fast as the static motion aftereffect. We tested predictions that follow from the hypothesised division in neuronal substrates. We found that for exactly the same adaptation conditions (oppositely directed transparent motion with different speeds), the aftereffect direction differs by 180 ° depending on the test pattern. The motion aftereffect is opposite to the pattern moving at low speed when the test pattern is static, and opposite to the high-speed pattern for a dynamic test pattern. The determining factor is the combination of adaptation speed and type of test pattern

On the velocity tuning of area 18 complex cell responses to moving textures

Unlike simple cells, complex cells of area 18 give a directionally selective response to motion of random textures, indicating that they may play a special role in motion detection. We therefore investigated how texture motion, and especially its velocity, is represented by area 18 complex cells. Do these cells have separable spatial and temporal tunings or are these nonseparable? To answer this question, we measured responses to moving random pixel arrays as a function of both pixel size and velocity, for a set of 63 directionally selective complex cells. Complex cells generally responded to a fairly wide range of pixel sizes and velocities. Variations in pixel size of the random pixel array only caused minor changes in the cells' preferred velocity. For nearly all cells the data much better fitted a model in which pixel size and velocity act separately, than a model in which pixel size and velocity interact so as to keep temporal-frequency sensitivity constant. Our conclusion is that the studied population of special complex cells in area 18 are true motion detectors, rather than temporal-frequency tuned neurons.</p

Velocity dependence of the interocular transfer of dynamic motion aftereffects

It is well established that motion aftereffects (MAEs) can show interocular transfer (IOT); that is, motion adaptation in one eye can give a MAE in the other eye. Different quantification methods and different test stimuli have been shown to give different IOT magnitudes, varying from no to almost full IOT. In this study, we examine to what extent IOT of the dynamic MAE (dMAE), that is the MAE seen with a dynamic noise test pattern, varies with velocity of the adaptation stimulus. We measured strength of dMAE by a nulling method. The aftereffect induced by adaptation to a moving random-pixel array was compensated (nulled), during a brief dynamic test period, by the same kind of motion stimulus of variable luminance signal-to-noise ratio (LSNR). The LSNR nulling value was determined in a Quest-staircase procedure. We found that velocity has a strong effect on the magnitude of IOT for the dMAE. For increasing speeds from 1.5 deg s-1 to 24 deg s-1 average IOT values increased about linearly from 18% to 63% or from 32% to 83%, depending on IOT definition. The finding that dMAEs transfer to an increasing extent as speed increases, suggests that binocular cells play a more dominant role at higher speeds

Dynamics of directional selectivity in area 18 and PMLS of the cat

Visual latencies and temporal dynamics of area 18 and PMLS direction-selective complex cells were defined with a reverse correlation method. The method allowed us to analyze the time course of responses to motion steps, without confounding temporal integration effects. Several measures of response latency and direction tuning dynamics were quantified: optimal latency (OL), latency of first and last significant responses (FSR, LSR), the increase and decrease of direction sensitivity in time, and the change of direction tuning in time. FSR, OL and LSR values for PMLS and area 18 largely overlapped. The small differences in mean latencies (3-4 ms for FSR and OL and 11.9 ms for the LSR) were not statistically significant. All cells in area 18 and the vast majority of cells in PMLS showed no systematic changes in preferred direction (monophasic neurons). In PMLS 5 out of 41 cells showed a reversal of preferred direction after ∼56 ms relative to their OL (biphasic neurons). Monophasic cells showed no systematic changes in direction tuning width during the interval from FSR to LSR. In both areas, development of direction sensitivity was significantly faster than return to the non direction sensitive state, but no significant difference was found between the two areas. We conclude that, for the monophasic type of direction-selective complex cells, the dynamics of primary motion processing are highly comparable for area 18 and PMLS. This suggests that motion information is predominantly processed in parallel, presumably based on input from the fast conducting thalamocortical Y-pathway.</p

Velocity dependence of the interocular transfer of dynamic motion aftereffects

It is well established that motion aftereffects (MAEs) can show interocular transfer (IOT); that is, motion adaptation in one eye can give a MAE in the other eye. Different quantification methods and different test stimuli have been shown to give different IOT magnitudes, varying from no to almost full IOT. In this study, we examine to what extent IOT of the dynamic MAE (dMAE), that is the MAE seen with a dynamic noise test pattern, varies with velocity of the adaptation stimulus. We measured strength of dMAE by a nulling method. The aftereffect induced by adaptation to a moving random-pixel array was compensated (nulled), during a brief dynamic test period, by the same kind of motion stimulus of variable luminance signal-to-noise ratio (LSNR). The LSNR nulling value was determined in a Quest-staircase procedure. We found that velocity has a strong effect on the magnitude of IOT for the dMAE. For increasing speeds from 1.5 deg s-1 to 24 deg s-1 average IOT values increased about linearly from 18% to 63% or from 32% to 83%, depending on IOT definition. The finding that dMAEs transfer to an increasing extent as speed increases, suggests that binocular cells play a more dominant role at higher speeds.</p