Biology subjects

Skinner, J.

Publications and source records attributed to Skinner, J..

3 recordsLinked to original sources

bioRxiv2018

Eye-position error influence over \"open-loop\" smooth pursuit initiation

The oculomotor system integrates a variety of visual signals into appropriate motor plans, but such integration can have widely varying time scales. For example, smooth pursuit eye movements to follow a moving target are slower and longer-lasting than saccadic eye movements, and it has been suggested that initiating a smooth pursuit eye movement involves an obligatory open-loop interval, in which new visual motion signals presumably cannot influence the ensuing motor plan for up to 100 ms after movement initiation. However, this view runs directly contrary to the idea that the oculomotor periphery has privileged access to short-latency visual signals. Here we show that smooth pursuit initiation is sensitive to visual inputs, even in \"open-loop\" intervals. We instructed male rhesus macaque monkeys to initiate saccade-free smooth pursuit eye movements, and we injected a transient, instantaneous eye position error signal at different times relative to movement initiation. We found robust short-latency modulations in eye velocity and acceleration, starting only [~]50 ms after transient signal occurrence, and even during \"open-loop\" pursuit initiation. Critically, the spatial direction of the injected position error signal had predictable effects on smooth pursuit initiation, with forward errors increasing eye acceleration and backwards errors reducing it. Catch-up saccade frequencies and amplitudes were also similarly altered [~]50 ms after transient signals, much like well-known effects on microsaccades during fixation. Our results demonstrate that smooth pursuit initiation is highly sensitive to visual signals, and that catch-up saccade generation is reset after a visual transient.

neuroscience↗

bioRxiv2018

The 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 directions. Maximum target displacement in the motion trajectory was 30 min arc (0.5 deg), 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, like 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 catalogue 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.

neuroscience↗

bioRxiv2017

Serial Crystallography with Multi-stage Merging of 1000’s of Images

KAMO and Blend provide particularly effective tools to manage automatically the merging of large numbers of datasets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending Blend to support additional clustering options such as use of more accurate cell distance metrics and use of reflection-intensity correlation coefficients to infer "distances" among sets of reflec- tions. This increases the sensitivity to differences in unit cell parameters and allows for clustering to assemble nearly complete datasets on the basis of intensity or ampli- tude differences. If datasets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. If starting from incomplete datasets, one applies KAMO twice, first using cell parameters. In this step we use either the simple cell vector distance of the original Blend, or we use the more sensi- tive NCDist. This step tends to find clusters of sufficient size so that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between the reflections having a common hkl to merge clusters in a way sensitive to structural differences that may not have perturbed the cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into cate- gories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent from one another, it should be possible to greatly improve the efficacy of data clustering software by using a multi-stage partitioning strategy. Here, we have demonstrated one possible approach to multi-stage data clustering. Our strategy is to use unit-cell clustering until merged data is sufficiently complete then to use intensity-based clustering. We have demonstrated that, using this strategy, we are able to accurately cluster datasets from crystals that have subtle differences.

bioinformatics↗