There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.

/pdf/the-neural-basis-of-decision-making-2p5agsty56.pdf

The Neural Basis of Decision Making

Logistic regression has been used as a supervised method for extracting EEG components predictive of binary perceptual decisions. However, often perceptual decisions require a choice between more than just two alternatives. In this paper we present results using multinomial logistic regression (MLR) for learning EEG components in a 3-way visual discrimination task. Subjects were required to decide between three object classes (faces, houses, and cars) for images which were embedded with varying amounts of noise. We recorded the subjects' EEG while they were performing the task and then used MLR to predict the stimulus category, on a single-trial basis, for correct behavioral responses. We found an early component (at 170ms) that was consistent across all subjects and with previous binary discrimination paradigms. However a later component (at 300-400ms), previously reported in the binary discrimination paradigms, was more variable across subjects in this three-way discrimination task. We also computed forward models for the EEG components, with these showing a difference in the spatial distribution of component activity for the different categorical decisions. In summary, we find that logistic regression, generalized to the arbitrary N-class case, can be a useful approach for learning and analyzing EEG components underlying multi-class perceptual decisions.

Learning EEG components for discriminating multi-class perceptual decisions

Depression is a serious mental illness that is frequently resistant to a first round of pharmacotherapy. Electroconvulsive therapy (ECT) is effective even for such treatment resistant depression but is associated with significant adverse effects. Repetitive transcranial magnetic stimulation (rTMS) over the left dorsolateral prefrontal cortex in comparison causes only mild discomfort but is less effective than ECT. We hypothesize that TMS treatment efficacy could be improved by locking TMS onset to a specific, potentially subject specific phase of the prefrontal alpha rhythm in the electroencephalogram (EEG). Here, we present an instrument that can track and predict phase of the alpha rhythm in the EEG to precisely target TMS while concurrently recording functional magnetic resonance imaging (fMRI) to study local and distributed hemodynamic brain responses to stimulation. Tests of the instrument with three healthy adults indicate that EEG phase-locked TMS can be administered accurately enough to start testing systematically whether specific stimulation protocols can lead to clinically significant improvements in depression. To our knowledge, this is the first system that can deliver TMS phase-locked to the alpha rhythm while concurrently recording fMRI. For patients, such EEG guided TMS treatment could lead to better clinical outcomes and lower incidence of adverse effects.

An EEG-fMRI-TMS instrument to investigate BOLD response to EEG guided stimulation

Coarse-grain parallel computing for very large scale neural simulations in the nexus simulation environment

In this paper we analyze a popular divisive nor- malization model of V 1 with respect to the relationship between its underlying coding strategy and the extraclassical physiolog- ical responses of its constituent modeled neurons. Specifically we are interested in whether the optimization goal of redun- dancy reduction naturally leads to reasonable neural responses, including reasonable distributions of responses. The model is trained on an ensemble of natural images and tested using sinusoidal drifting gratings, with metrics such as suppression index and contrast dependent receptive field growth compared to the objective function values for a sample of neurons. We find that even though the divisive normalization model can produce "typical" neurons that agree with some neurophysiology data, distributions across samples do not agree with experimental data. Our results suggest that redundancy reduction itself is not necessarily causal of the observed extraclassical receptive field phenomena, and that additional optimization dimensions and/or biological constraints must be considered.

Analysis of a gain control model of V1: is the goal redundancy reduction?

The authors present neural network simulations of how the visual cortex may segment objects and bind attributes based on depth-from-occlusion. They briefly discuss one particular subprocess in the occlusion-based model most relevant to segmentation and binding: determination of the direction of figure. They propose that the model allows addressing a central issue in object recognition: how the visual system defines an object. In addition, the model was tested on illusory stimuli, with the network's response indicating the existence of robust psychophysical properties in the system. >

Paul Sajda

Papers

Learning EEG components for discriminating multi-class perceptual decisions

An EEG-fMRI-TMS instrument to investigate BOLD response to EEG guided stimulation

Coarse-grain parallel computing for very large scale neural simulations in the nexus simulation environment

Analysis of a gain control model of V1: is the goal redundancy reduction?

A neural network model of object segmentation and feature binding in visual cortex