Raymond J. Dolan

Bio: Raymond J. Dolan is an academic researcher from University College London. The author has contributed to research in topics: Prefrontal cortex & Functional magnetic resonance imaging. The author has an hindex of 196, co-authored 919 publications receiving 138540 citations. Previous affiliations of Raymond J. Dolan include VU University Amsterdam & McGovern Institute for Brain Research.

Emotion-induced retrograde amnesia varies as a function of noradrenergic-glucocorticoid activity

Abstract: Rationale Privileged episodic encoding of an aversive event often comes at a cost of neutral events flanking the aversive event, resulting in decreased episodic memory for these neutral events. This peri-emotional amnesia is amygdala-dependent and varies as a function of norepinephrine activity. However, less is known about the amnesiogenic potential of cortisol.

Enhanced alpha-oscillations in visual cortex during anticipation of self-generated visual stimulation

Abstract: The perceived intensity of sensory stimuli is reduced when these stimuli are caused by the observer's actions. This phenomenon is traditionally explained by forward models of sensory action—outcome, which arise from motor processing. Although these forward models critically predict anticipatory modulation of sensory neural processing, neurophysiological evidence for anticipatory modulation is sparse and has not been linked to perceptual data showing sensory attenuation. By combining a psychophysical task involving contrast discrimination with source-level time—frequency analysis of MEG data, we demonstrate that the amplitude of alpha-oscillations in visual cortex is enhanced before the onset of a visual stimulus when the identity and onset of the stimulus are controlled by participants' motor actions. Critically, this prestimulus enhancement of alpha-amplitude is paralleled by psychophysical judgments of a reduced contrast for this stimulus. We suggest that alpha-oscillations in visual cortex preceding self-generated visual stimulation are a likely neurophysiological signature of motor-induced sensory anticipation and mediate sensory attenuation. We discuss our results in relation to proposals that attribute generic inhibitory functions to alpha-oscillations in prioritizing and gating sensory information via top—down control.

How the Brain Translates Money into Force

Abstract: Unconscious motivation in humans is often inferred but rarely demonstrated empirically. Weimaged motivational processes, implemented in a paradigm that varied the amount andreportability of monetary rewards for which subjects exerted physical effort. We show that, evenwhen subjects cannot report how much money is at stake, they nevertheless deploy more force forhigher amounts. Such a motivational effect is underpinned by engagement of a specific basalforebrain region. Our findings thus reveal this region as a key node in brain circuitry that enablesexpected rewards to energize behavior, without the need for the subjects’ awareness.Humans tend to adapt the degree of effort they expend according to the magnitude of rewardthey expect. Such a process has been proposed as an operant concept of motivation (1-3).Motivational processes may be obvious, as when a prospector spends days in extremeconditions seeking gold. The popular view is that motivation can also be unconscious, suchthat a person may be unable to report the goals or rewards that drive a particular behavior.However, empirical evidence on this issue is lacking, and the potential brain mechanismsinvolved in converting expected rewards into behavioral activation are poorly understood.We developed an experimental paradigm to visualize unconscious motivational processes,using functional magnetic resonance imaging. A classical approach to trigger unconsciousprocessing is subliminal stimulation, which can be implemented by means of maskingprocedures. The terminology we use in this report is based on a recent taxonomy (4), inwhich a process is considered subliminal if it is attended but not reportable. Successful brainimaging studies of subliminal processes have focused so far on processing words (5, 6) aswell as emotional stimuli (7, 8). In our study, the object of masking was an incentivestimulus for a future action, represented by the amount of reward at stake. The question weasked is whether, and how, the human brain energizes behavior in proportion to subliminalincentives.We developed an incentive force task, using money as a reward: a manipulation that isconsistently shown to activate reward circuits in the human brain (9-11). The exact level ofmotivation was manipulated by randomly assigning the amount at stake as one pound or onepenny. Pictures of the corresponding coins were displayed on a computer screen at thebeginning of each trial, between two screenshots of “mask” images (Fig. 1). The reportabiity

Reinforcement Learning: An Introduction

Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

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

Abstract: 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。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

An integrative theory of prefrontal cortex function

Abstract: ▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.