Biomedical subjects

Martin J M Lankheet

Publications and source records attributed to Martin J M Lankheet.

7 recordsLinked to original sources

PubMed2005

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.

Animals↗

PubMed2004

Temporal dynamics of direction tuning in motion-sensitive macaque area MT.

We studied the temporal dynamics of motion direction sensitivity in macaque area MT using a motion reverse correlation paradigm. Stimuli consisted of a random sequence of motion steps in eight different directions. Cross-correlating the stimulus with the resulting neural activity reveals the temporal dynamics of direction selectivity. The temporal dynamics of direction selectivity at the preferred speed showed two phases along the time axis: one phase corresponding to an increase in probability for the preferred direction at short latencies and a second phase corresponding to a decrease in probability for the preferred direction at longer latencies. The strength of this biphasic behavior varied between neurons from weak to very strong and was uniformly distributed. Strong biphasic behavior suggests optimal responses for motion steps in the antipreferred direction followed by a motion step in the preferred direction. Correlating spikes to combinations of motion directions corroborates this distinction. The optimal combination for weakly biphasic cells consists of successive steps in the preferred direction, whereas for strongly biphasic cells, it is a reversal of directions. Comparing reverse correlograms to combinations of stimuli to predictions based on correlograms for individual directions revealed several nonlinear effects. Correlations for successive presentations of preferred directions were smaller than predicted, which could be explained by a static nonlinearity (saturation). Correlations to pairs of (nearly) opposite directions were larger than predicted. These results show that MT neurons are generally more responsive when sudden changes in motion directions occur, irrespective of the preferred direction of the neurons. The latter nonlinearities cannot be explained by a simple static nonlinearity at the output of the neuron, but most likely reflect network interactions.

Action Potentials↗

PubMed2004

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 approximately 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.

Animals↗

PubMed2004

Binocular correlation does not improve coherence detection for fronto-parallel motion.

We studied the low-level interactions between motion coherence detection and binocular correlation detection. It is well-established that e.g. depth information from motion parallax and from binocular disparities is effectively integrated. The question we aimed to answer is whether such interactions also exist at the very first correlation level that both mechanisms might have in common. First we quantitatively compared motion coherence detection and binocular correlation detection using similar stimuli (random pixels arrays, RPAs) and the same noise masking paradigm (luminance signal to noise ratio, LSNR). This showed that human observers are much more sensitive to motion than to binocular correlation. Adding noise therefore has a much stronger effect on binocular correlation than on motion detection. Next we manipulated the shape of the stimulus aperture to equalize LSNR thresholds for motion and binocular correlation. Motion sensitivity could be progressively reduced by shortening the length of the motion path, while keeping the aperture area constant. Changing the shape of the aperture did not affect binocular correlation sensitivity. A 'balanced' stimulus, one with equal strengths of motion and binocular correlation signals was then used to study the mutual interactions. In accordance with previous results, motion was found to greatly facilitate binocular correlation. Binocular correlation, however did not facilitate motion detection. We conclude that interactions are asymmetrical; fronto-parallel motion is primarily detected monocularly and this information can then be used to facilitate binocular correlation, but binocular correlation cannot improve motion sensitivity.

Adult↗

PubMed2003

The motion reverse correlation (MRC) method: a linear systems approach in the motion domain.

We introduce the motion reverse correlation method (MRC), a novel stimulus paradigm based on a random sequence of motion impulses. The method is tailored to investigate the spatio-temporal dynamics of motion selectivity in cells responding to moving random dot patterns. Effectiveness of the MRC method is illustrated with results obtained from recordings in both anesthetized cats and an awake, fixating macaque monkey. Motion tuning functions are computed by reverse correlating the response of single cells with a rapid sequence of displacements of a random pixel array (RPA). Significant correlations between the cell's responses and various aspects of stimulus motion are obtained at high temporal resolution. These correlations provide a detailed description of the temporal dynamics of, for example, direction tuning and velocity tuning. In addition, with a spatial array of independently moving RPAs, the MRC method can be used to measure spatial as well as temporal receptive field properties. We demonstrate that MRC serves as a powerful and time-efficient tool for quantifying receptive field properties of motion selective cells that yields temporal information that cannot be derived from existing methods.

Animals↗

PubMed2003

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.

Adaptation, Physiological↗

PubMedDate not supplied

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.

Animals↗