App-Based Saccade Latency and Directional Error Determination Across the Adult Age Spectrum.

Objective: We aid in neurocognitive monitoring outside the hospital environment by enabling app-based measurements of visual reaction time (saccade latency) and directional error rate in a cohort of subjects spanning the adult age spectrum.

Methods: We developed an iOS app to record subjects with the frontal camera during pro- and anti-saccade tasks. We further developed automated algorithms for measuring saccade latency and directional error rate that take into account the possibility that it might not always be possible to determine the eye movement from app-based recordings.

Freely scalable and reconfigurable optical hardware for deep learning.

As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values.

Measuring Saccade Latency Using Smartphone Cameras.

Objective: Accurate quantification of neurodegenerative disease progression is an ongoing challenge that complicates efforts to understand and treat these conditions. Clinical studies have shown that eye movement features may serve as objective biomarkers to support diagnosis and tracking of disease progression. Here, we demonstrate that saccade latency-an eye movement measure of reaction time-can be measured robustly outside of the clinical environment with a smartphone camera.

Determination of Saccade Latency Distributions using Video Recordings from Consumer-grade Devices.

Quantitative and accurate tracking of neurocognitive decline remains an ongoing challenge. We seek to address this need by focusing on robust and unobtrusive measurement of saccade latency - the time between the presentation of a visual stimulus and the initiation of an eye movement towards the stimulus - which has been shown to be altered in patients with neurocognitive decline or neurodegenerative diseases. Here, we present a novel, deep convolutional-neuralnetwork-based method to measure saccade latency outside of the clinical environment using a smartphone camera without the need for supplemental or special-purpose illumination.