Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons

The adaptive leaky integrate-and-fire (ALIF) model is fundamental within computational neuroscience and has been instrumental in studying our brains $\textit{in silico}$. Due to the sequential nature of simulating these neural models, a commonly faced issue is the speed-accuracy trade-off: either accurately simulate a neuron using a small discretisation time-step (DT), which is slow, or more quickly simulate a neuron using a larger DT and incur a loss in simulation accuracy. Here we provide a solution to this dilemma, by algorithmically reinterpreting the ALIF model, reducing the sequential simulation complexity and permitting a more efficient parallelisation on GPUs. We computationally validate our implementation to obtain over a $50\times$ training speedup using small DTs on synthetic benchmarks. We also obtained a comparable performance to the standard ALIF implementation on different supervised classification tasks - yet in a fraction of the training time. Lastly, we showcase how our model makes it possible to quickly and accurately fit real electrophysiological recordings of cortical neurons, where very fine sub-millisecond DTs are crucial for capturing exact spike timing.Comment: 15 pages, 5 figure

Integrating Glenohumeral Range of Motion with Dynamic Postural Control for Early Detection of Elbow Injury Risk in Collegiate Baseball Pitchers: A Preliminary Prospective Case Series

Introduction: Medial elbow ulnar collateral ligament (UCL) injuries are common in baseball with increased surgical incidence in pitchers. There is no consensus on which modifiable injury risk metrics or performance screening tools to use to identify at risk collegiate baseball pitchers. Current literature on UCL injury risk has focused on local passive glenohumeral rotational motion with limited forays into more global movement patterns. The aim of this study was to assess possible relationships involving UCL injuries and modifiable local and global motor control performance risk factors. Material and Methods: An observational single cohort case series involving 15 collegiate baseball pitchers aimed to elucidate disparitites in glenohumeral active and passive range of motion alongside an assessment of general movement competency and dynamic motor control performance to identify injury risk for the UCL. Preseaon baseline metrics, including the Functional Movement Screen, Upper and Lower Quarter Y Balance Test, and glenohumaral internal and external rotation active, passive, and total arc range of motion were examined. Results: The two pitchers with UCL injury demonstrated less ability to actively move into their available passive rotational range with decreased lower extremity dynamic postural control, but no difference with general movement competency. The pitcher requiring UCL surgical intervention demonstrated even greater excessive passive range compared to the other injured pitcher and a decreased ability to control base of support during the dynamic single-leg balance performance test. Discussion: Participants with UCL injuries exibited noticable deficiencies in baseline active and passive mobility, suggestive of a potential motor control deficit and a compromised ability to fully access available range of motion. Furthermore, diminished performance on the Lower Quarter Y Balance Test identified potential motor control limitations involving a decreased ability to dynamically maintain a base of support. Conclusions: This is the first study designed to identify motor control issues in the both the local shoulder region and global body movement to identify modifiable risk factors for UCL injuries in baseball pitchers. This study may begin to address the gap in the literature by combining modifiable local shoulder range of motion and global balance metrics to identify pitchers at risk for UCL injuries. It underscores the significance of comparing active and passive range of motion in tandem with dynamic postural control assessments to better evaluate the risk of injuries in baseball pitchers. This case series lays the groundwork for future randomized clinical trials to evaluate the utiliity of combining this information

Hierarchical temporal prediction captures motion processing along the visual pathway

Visual neurons respond selectively to features that become increasingly complex from the eyes to the cortex. Retinal neurons prefer flashing spots of light, primary visual cortical (V1) neurons prefer moving bars, and those in higher cortical areas favor complex features like moving textures. Previously, we showed that V1 simple cell tuning can be accounted for by a basic model implementing temporal prediction – representing features that predict future sensory input from past input (Singer et al., 2018). Here, we show that hierarchical application of temporal prediction can capture how tuning properties change across at least two levels of the visual system. This suggests that the brain does not efficiently represent all incoming information; instead, it selectively represents sensory inputs that help in predicting the future. When applied hierarchically, temporal prediction extracts time-varying features that depend on increasingly high-level statistics of the sensory input

Blueberry Drone AI: Estimating Crop Yield using Deep Learning & Smart Drones

This project seeks to assist blueberry growers in New Jersey estimate crop yield by developing software that allows autonomous drones to capture aerial images of blueberry bushes in the field, perform berry count, and identify blueberry conditions using deep learning models & computer vision

Seeking Asylum Adolescents Explore the Crossroads of Human Rights Education and Cosmopolitan Critical Literacy

Urban middle school students utilize human rights education and cosmopolitan critical literacy to explore local and global issues around immigrants and refugees through the production of a BYOT short film

Neuroimaging of Sudden Unexpected Death in Epilepsy (SUDEP): Insights From Structural and Resting-State Functional MRI Studies

The elusive nature of sudden unexpected death in epilepsy (SUDEP) has led to investigations of mechanisms and identification of biomarkers of this fatal scenario that constitutes the leading cause of premature death in epilepsy. In this short review, we compile evidence from structural and functional neuroimaging that demonstrates alterations to brain structures and networks involved in central autonomic and respiratory control in SUDEP and those at elevated risk. These findings suggest that compromised central control of vital regulatory processes may contribute to SUDEP. Both structural changes and dysfunctional interactions indicate potential mechanisms underlying the fatal event; contributions to individual risk prediction will require further study. The nature and sites of functional disruptions suggest potential non-invasive interventions to overcome failing processes

Livestock Protection Tools for California Ranchers

Conflicts between livestock and predators are perhaps inevitable, especially on extensively managed rangelands This publication helps producers evaluate livestock lethal and non-lethal protection tools that may fit their site-specific needs