Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

/pdf/sequential-monte-carlo-methods-in-practice-3kfxeebn8e.pdf

Sequential Monte Carlo methods in practice

Information Theory and Reliable Communication

Population-based epidemiologic studies have uncovered the high prevalence and wide severity spectrum of undiagnosed obstructive sleep apnea, and have consistently found that even mild obstructive sleep apnea is associated with significant morbidity. Evidence from methodologically strong cohort studies indicates that undiagnosed obstructive sleep apnea, with or without symptoms, is independently associated with increased likelihood of hypertension, cardiovascular disease, stroke, daytime sleepiness, motor vehicle accidents, and diminished quality of life. Strategies to decrease the high prevalence and associated morbidity of obstructive sleep apnea are critically needed. The reduction or elimination of risk factors through public health initiatives with clinical support holds promise. Potentially modifiable risk factors considered in this review include overweight and obesity, alcohol, smoking, nasal congestion, and estrogen depletion in menopause. Data suggest that obstructive sleep apnea is associated with all these factors, but at present the only intervention strategy supported with adequate evidence is weight loss. A focus on weight control is especially important given the expanding epidemic of overweight and obesity in the United States. Primary care providers will be central to clinical approaches for addressing the burden and the development of cost-effective case-finding strategies and feasible treatment for mild obstructive sleep apnea warrants high priority.

Epidemiology of obstructive sleep apnea: a population health perspective.

International classification of sleep disorders-third edition: highlights and modifications.

Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of "sleep and memory" research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.

https://www.zora.uzh.ch/id/eprint/77770/1/physiol_rev_93.pdf

About sleep's role in memory

The possibility of preventing functional impairment due to sleep loss by pharmacologically enhancing sleep.

Chapter 137 – Unipolar Major Depression

Hibernation is a unique metabolic adaptation employed by several animal species for survival where its study would further enhance our understanding of metabolic disorders, such as diabetes and obesity. As a primate animal with close genetic ties to humans, the recent discovery of hibernation in dwarf lemurs of Madagascar has attracted the attention of researchers. Traditional recording systems require the physical tethering of the animals to the recording apparatus or the use of implantable devices. Scalp and needle electrodes interfere with the natural hibernation process and limit the continuity of the experiments, while invasive procedures are banned on endangered species. By integrating a full-wave rectifier, low-noise signal conditioning circuit, frequency modulation transmitter, and antenna in a single application specific integrated circuit (ASIC), we have developed an ultra-miniaturized wireless system that measures $34 \times 4 \times 2.6$ mm3 in volume. It only requires three off-chip components (a coil wound around a ferrite rod and two external capacitors) to be powered wirelessly through a 1-MHz inductive link, such that it can be packaged inside a glass or polymer capsule and injected subcutaneously underneath the scalp or chest without requiring a surgery, thereby addressing the shortcomings of the traditional monitoring systems. Our recording device provides an input/output correlation coefficient greater than 80% for input amplitudes ranging from 60 to 260 $\mu V_{\mathrm{ pp}}$ , with a wireless data transmission range of ~2.5 cm while operating near the 902–928 MHz ISM frequency band. This system would enable future studies of electroencephalography and electrocardiography in hibernating dwarf lemurs. The ASIC was fabricated using the ON Semiconductor 0.5- $\mu \text{m}$ CMOS process with an active area of 2.5 $\times $ 1 mm2 and has a power consumption of 7.75 mW from a 3.1 V supply. In this paper, we demonstrate the in vitro functionality of the system using simulated physiological signals directly applied to the ASIC or through standard stainless steel electrodes immersed in saline solution.

In Vitro Evaluation of an Injectable EEG/ECG Sensor for Wireless Monitoring of Hibernation in Endangered Animal Species

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major depressive disorder (MDD), most studies to date have administered continuous or open-loop stimulation with promising but mixed results. One factor contributing to these mixed results may stem from when the stimulation is applied. Stimulation administration specific to high-symptom states in a personalized and responsive manner may be more effective at reducing symptoms compared to continuous stimulation and may avoid diminished therapeutic effects related to habituation. Additionally, a lower total duration of stimulation per day is advantageous for reducing device energy consumption. This protocol describes an experimental workflow using a chronically implanted neurostimulation device to achieve closed-loop stimulation for individuals with treatment-refractory MDD. This paradigm hinges on determining a patient-specific neural biomarker that is related to states of high symptoms and programming the device detectors, such that stimulation is triggered by this read-out of symptom state. The described procedures include how to obtain neural recordings concurrent with patient symptom reports, how to use these data in a state-space model approach to differentiate low- and high-symptom states and corresponding neural features, and how to subsequently program and tune the device to deliver closed-loop stimulation therapy. 

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

The few pharmacological treatments shown to be effective in reducing sleep disturbance in posttraumatic stress disorder (PTSD) might work through normalization of rapid eye movement (REM). However, evidence of REM sleep disturbance in PTSD has been inconsistent and the definition of REM bout has varied as well. In this study, we compared polysomnographic findings in adults with PTSD to both normal sleepers and insomniacs. We found no differences between those with and without PTSD on REM bout frequency or duration. We did, however, find gender differences within our PTSD sample as consistent with a previous review suggesting that males with PTSD are more likely to demonstrate REM sleep disturbance. Consensus on REM bout definition is needed, in addition to studies powered to detect gender differences.

REM Sleep Bout Duration and Frequency in PTSD

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