Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning

Vulnerabilities in first-generation RFID-enabled credit cards

Credit cards ; Radio frequency identification systems

Factors associated with post-arrest withdrawal of life-sustaining therapy.

INTRODUCTION: Most successfully resuscitated cardiac arrest patients do not survive to hospital discharge. Many have withdrawal of life sustaining therapy (WLST) as a result of the perception of poor neurologic prognosis. The characteristics of these patients and differences in their post-arrest care are largely unknown. METHODS: Utilizing the Penn Alliance for Therapeutic Hypothermia Registry, we identified a cohort of 1311 post-arrest patients from 26 hospitals from 2010 to 2014 who remained comatose after return of spontaneous circulation. We stratified patients by whether they had WLST post-arrest and analyzed demographic, arrest, and post-arrest variables. RESULTS: In our cohort, 565 (43%) patients had WLST. In multivariate regression, patients who had WLST were less likely to go to the cardiac catheterization lab (OR 0.40; 95% CI: 0.26-0.62) and had shorter hospital stays (OR 0.93; 95% CI: 0.91-0.95). When multivariate regression was limited to patient demographics and arrest characteristics, patients with WLST were older (OR 1.18; 95% CI: 1.07-1.31 by decade), had a longer arrest duration (OR 1.14; 95% CI: 1.05-1.25 per 10min), more likely to be female (OR: 1.41; 95% CI: 1.01-1.96), and less likely to have a witnessed arrest (OR 0.65; 95% CI: 0.42-0.98). CONCLUSION: Patients with WLST differ in terms of demographic, arrest, and post-arrest characteristics and treatments from those who did not have WLST. Failure to account for this variability could affect both clinical practice and the interpretation of research

MotionLM: Multi-Agent Motion Forecasting as Language Modeling

Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain. Our model, MotionLM, provides several advantages: First, it does not require anchors or explicit latent variable optimization to learn multimodal distributions. Instead, we leverage a single standard language modeling objective, maximizing the average log probability over sequence tokens. Second, our approach bypasses post-hoc interaction heuristics where individual agent trajectory generation is conducted prior to interactive scoring. Instead, MotionLM produces joint distributions over interactive agent futures in a single autoregressive decoding process. In addition, the model's sequential factorization enables temporally causal conditional rollouts. The proposed approach establishes new state-of-the-art performance for multi-agent motion prediction on the Waymo Open Motion Dataset, ranking 1st on the interactive challenge leaderboard.Comment: To appear at the International Conference on Computer Vision (ICCV) 202

Stem-Loop Recognition by DDX17 Facilitates miRNA Processing and Antiviral Defense

SummaryDEAD-box helicases play essential roles in RNA metabolism across species, but emerging data suggest that they have additional functions in immunity. Through RNAi screening, we identify an evolutionarily conserved and interferon-independent role for the DEAD-box helicase DDX17 in restricting Rift Valley fever virus (RVFV), a mosquito-transmitted virus in the bunyavirus family that causes severe morbidity and mortality in humans and livestock. Loss of Drosophila DDX17 (Rm62) in cells and flies enhanced RVFV infection. Similarly, depletion of DDX17 but not the related helicase DDX5 increased RVFV replication in human cells. Using crosslinking immunoprecipitation high-throughput sequencing (CLIP-seq), we show that DDX17 binds the stem loops of host pri-miRNA to facilitate their processing and also an essential stem loop in bunyaviral RNA to restrict infection. Thus, DDX17 has dual roles in the recognition of stem loops: in the nucleus for endogenous microRNA (miRNA) biogenesis and in the cytoplasm for surveillance against structured non-self-elements

The Association between Supraphysiologic Arterial Oxygen Levels and Mortality in Critically Ill Patients. A Multicenter Observational Cohort Study.

Rationale: There is conflicting evidence on harm related to exposure to supraphysiologic PaO2 (hyperoxemia) in critically ill patients.Objectives: To examine the association between longitudinal exposure to hyperoxemia and mortality in patients admitted to ICUs in five United Kingdom university hospitals.Methods: A retrospective cohort of ICU admissions between January 31, 2014, and December 31, 2018, from the National Institute of Health Research Critical Care Health Informatics Collaborative was studied. Multivariable logistic regression modeled death in ICU by exposure to hyperoxemia.Measurements and Main Results: Subsets with oxygen exposure windows of 0 to 1, 0 to 3, 0 to 5, and 0 to 7 days were evaluated, capturing 19,515, 10,525, 6,360, and 4,296 patients, respectively. Hyperoxemia dose was defined as the area between the PaO2 time curve and a boundary of 13.3 kPa (100 mm Hg) divided by the hours of potential exposure (24, 72, 120, or 168 h). An association was found between exposure to hyperoxemia and ICU mortality for exposure windows of 0 to 1 days (odds ratio [OR], 1.15; 95% compatibility interval [CI], 0.95-1.38; P = 0.15), 0 to 3 days (OR 1.35; 95% CI, 1.04-1.74; P = 0.02), 0 to 5 days (OR, 1.5; 95% CI, 1.07-2.13; P = 0.02), and 0 to 7 days (OR, 1.74; 95% CI, 1.11-2.72; P = 0.02). However, a dose-response relationship was not observed. There was no evidence to support a differential effect between hyperoxemia and either a respiratory diagnosis or mechanical ventilation.Conclusions: An association between hyperoxemia and mortality was observed in our large, unselected multicenter cohort. The absence of a dose-response relationship weakens causal interpretation. Further experimental research is warranted to elucidate this important question