Sub-second timing irregularities in a simple motor task in autism spectrum disorder: Preliminary effects of intermittent light stimulation

Several authors have contributed extensively to the neurocognitive understanding of timing. In Autism Spectrum Disorder (ASD) on the contrary, internal timing and its functioning is not well understood. In this study, we have adapted a simple finger-tapping motor task, with a timing component, as we aim at understanding whether the processing of time is preserved in this population. We have tested a group of people on the autism spectrum without intellectual disabilities and a control sample recruited from the general population, matched for age, sex, schooling and general cognitive abilities on this task with a learning and testing phase. In the testing phase, we have added two exploratory conditions where participants were exposed to intermittent light stimulation of 4 and 8 Hz. Results show that both in the learning and testing phase, besides troubles in the motor component encountered by the people on the spectrum, their timing component performance was also problematic. This reveals to be especially true for time intervals below the 1 s range, as hypothesized, whereas performance in longer intervals is clearly preserved. It was also observed that the exposure to intermittent light stimulation specifically overcomes the difficulties observed in the autistic group, at the timing components at this millisecond time range. The observed timing difficulties in this group seem to be restricted to the system responsible for the processing of time intervals in the milliseconds range, which helps accommodate disparate findings in the literature.

Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions

While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics.

Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations

Electronic health records (EHRs) contain important temporal information about the progression of disease and treatment outcomes. This paper proposes a transitive sequencing approach for constructing temporal representations from EHR observations for downstream machine learning. Using clinical data from a cohort of patients with congestive heart failure, we mined temporal representations by transitive sequencing of EHR medication and diagnosis records for classification and prediction tasks. We compared the classification and prediction performances of the transitive sequential representations (bag-of-sequences approach) with the conventional approach of using aggregated vectors of EHR data (aggregated vector representation) across different classifiers. We found that the transitive sequential representations are better phenotype "differentiators" and predictors than the "atemporal" EHR records. Our results also demonstrated that data representations obtained from transitive sequencing of EHR observations can present novel insights about the progression of the disease that are difficult to discern when clinical data are treated independently of the patient's history.