Method for Determining the Estimated Timing Uncertainty for Digital Sampling Instruments

The overall uncertainty in digital captured data points is often misunderstood in our organization and is typically accepted as only the manufacturer uncertainty specification of the time base clock typically on the order of 10-100 parts per million. The time base clock of digital sampling technologies is critically important to maintain timing control of the internal electronics and to achieve the specified sampling rate of the instrument. The time base clock must remain within the manufacturer specification tolerance throughout the calibration interval to assure accurate performance. However, the time base uncertainty does not adequately account for the additional measurement errors accompanying the capture and evaluation of the time values for any cardinal points of interest when periodically sampling analog waveforms generated by other instruments or Units Under Test (UUTs). The proposed methodology described here details a general approach used to estimate the magnitude of the digital instrument sampling error when capturing analog waveforms based upon the instrument sampling rate, the frequency of a nominally equivalent sinusoidal waveform, as well as, whether the time value of any cardinal points is selected by a ‘Next Point After’ or Interpolation method for our purposes. Finally, the overall estimated timing uncertainty is quantified by arithmetically combining the error contributions for the sampling rate, the cardinal point selection method, and the instrument time base specification. The results of this method aid in selecting the appropriate digital sampling technology based upon waveform rise time requirements and provide general engineering guidance. Since the estimated error is a portion of the sampling timestep interval, the percentage error could be significant based upon the measured rise time. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy National Nuclear Security Administration under contract DE-NA0003525.

How do packet losses affect measures of averaged neural signals?

Recent advances in implanted device development have enabled chronic streaming of neural data to external devices allowing for long timescale, naturalistic recordings. However, characteristic data losses occur during wireless transmission. Estimates for the duration of these losses are typically uncertain reducing signal quality and impeding analyses. To characterize the effect of these losses on recovery of averaged neural signals, we simulated neural time series data for a typical event-related potential (ERP) experiment. We investigated how the signal duration and the degree of timing uncertainty affected the offset of the ERP, its duration in time, its amplitude, and the ability to resolve small differences corresponding to different task conditions. Simulations showed that long timescale signals were generally robust to the effects of packet losses apart from timing offsets while short timescale signals were significantly delocalized and attenuated. These results provide clarity on the types of signals that can be resolved using these datasets and provide clarity on the restrictions imposed by data losses on typical analyses.

Dissociating the effect of reward uncertainty and timing uncertainty on neural indices of reward prediction errors: A reward positivity (RewP) event-related potential (ERP) study

Accurate reward predictions include forecasting both what a reward will be and when a reward will occur. We tested how variations in the certainty of reward outcome and certainty in timing of feedback presentation modulate neural indices of reward prediction errors using the reward positivity (RewP) component of the scalp-recorded brain event-related potential (ERP). In a within-subjects design, seventy-three healthy individuals completed two versions of a cued doors task; one cued the probability of a reward outcome while the other cued the probability of a delay before feedback. Replicating previous results, RewP amplitude was larger for uncertain feedback compared to certain feedback. Additionally, RewP amplitude was differentially associated with uncertainty of presence/absence of reward, but not uncertainty of feedback timing. Findings suggest a dissociation in that RewP amplitude is modulated by reward prediction certainty but is less affected by certainty surrounding timing of feedback.

Serotonergic projections to the orbitofrontal and medial prefrontal cortices differentially modulate waiting for future rewards

Optogenetic activation of serotonergic neurons in the dorsal raphe nucleus (DRN) enhances patience when waiting for future rewards, and this effect is maximized by both high probability and high timing uncertainty of reward. Here, we explored which serotonin projection areas contribute to these effects using optogenetic axon terminal stimulation. We found that serotonin stimulation in the orbitofrontal cortex (OFC) is nearly as effective as that in the DRN for promoting waiting, while in the nucleus accumbens, it does not promote waiting. We also found that serotonin stimulation in the medial prefrontal cortex (mPFC) promotes waiting only when the timing of future rewards is uncertain. Our Bayesian decision model of waiting assumed that the OFC and mPFC calculate the posterior probability of reward delivery separately. These results suggest that serotonin in the mPFC affects evaluation of time committed, while serotonin in the OFC is responsible for overall valuation of delayed rewards.

Time sequence of TGFs and optical pulses detected by ASIM

<p>In 2018, the Atmospheric Space Interactions Monitor (ASIM) was launched and mounted onboard the Columbus module of the International Space Station (ISS). Using data from the Modular X- and Gamma-Ray Sensor (MXGS) and the Modular Multispectral Imaging Array (MMIA), we investigate the time sequence of the TGFs detected by MXGS and the optical pulses detected by the MMIA. The optical pulses are observed in the 337 nm and 777.4 nm, and the X- and gamma-rays are detected by the High Energy Detector of MXGS, which is sensitive to energies from 300 keV to more than 30 MeV. We will also look into the TGF duration and any correlation with the time between the TGFs and the main optical signals. The data used is from June 2018 (shortly after mounting on the Columbus module) until the end of March 2019, when the relative timing uncertainty between the two instruments was +/- 80 us. The data after this is presented in Skeie et al.</p>