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.

Reward Facilitates Tactile Judgments and Modulates Hemodynamic Responses in Human Primary Somatosensory Cortex

Abstract: Reinforcing effects of reward on action are well established, but possible effects on sensory function are less well explored. Here, using functional magnetic resonance imaging, we assessed whether reward can influence somatosensory judgments and modulate activity in human somatosensory cortex. Participants discriminated electrical somatosensory stimuli on an index finger with correct performance rewarded financially at trial end, at one of four different anticipated levels. Higher rewards improved tactile performance and led to increased hemodynamic signals from ventral striatum on rewarded trials. Remarkably, primary somatosensory cortex contralateral to the judged hand was reactivated at the point of reward delivery, despite the absence of concurrent somatosensory input at that time point. This side-specific reactivation of primary somatosensory cortex increased monotonically with level of reward. Moreover, the level of reward received on a particular trial influenced somatosensory performance and neural activity on the subsequent trial, with better discrimination and enhanced hemodynamic response in contralateral primary somatosensory cortex for trials that followed higher rewards. These results indicate that rewards can influence not only classical reward-related regions, but also early somatosensory cortex when a decision is required for that modality.

Cholinesterase inhibition modulates visual and attentional brain responses in Alzheimer's disease and health

Abstract: Visuo-attentional deficits occur early in Alzheimer's disease (AD) and are considered more responsive to pro-cholinergic therapy than characteristic memory disturbances. We hypothesised that neural responses in AD during visuo-attentional processing would be impaired relative to controls, yet partially susceptible to improvement with the cholinesterase inhibitor physostigmine. We studied 16 mild AD patients and 17 age-matched healthy controls, using fMRI-scanning to enable within-subject placebo-controlled comparisons of effects of physostigmine on stimulus- and attention- related brain activations, plus between-group comparisons for these. Subjects viewed face or building stimuli while performing a shallow judgement (colour of image) or a deep judgement (young/old age of depicted face or building). Behaviourally, AD subjects performed slower than controls in both tasks, while physostigmine benefited the patients for the more demanding age-judgement task. Stimulus-selective (face minus building, and vice versa) BOLD signals in precuneus and posterior parahippocampal cortex were attenuated in patients relative to controls, but increased following physostigmine. By contrast, face-selective responses in fusiform cortex were not impaired in AD and showed decreases following physostigmine for both groups. Task-dependent responses in right parietal and prefrontal cortices were diminished in AD but improved following physostigmine. A similar pattern of group and treatment effects was observed in two extrastriate cortical regions that showed physostigmine-induced enhancement of stimulus-selectivity for the deep versus shallow task. Finally, for the healthy group, physostigmine decreased stimulus and task-dependent effects, partly due to an exaggeration of selectivity during the shallow relative to deep task. The differences in brain activations between groups and treatments were not attributable merely to performance (reaction time) differences. Our results demonstrate that physostigmine can improve both stimulus- and attention-dependent responses in functionally affected extrastriate and frontoparietal regions in AD, while perturbing the normal pattern of responses in many of the same regions in healthy controls.

Action controls dopaminergic enhancement of reward representations

Abstract: Dopamine is widely observed to signal anticipation of future rewards and thus thought to be a key contributor to affectively charged decision making. However, the experiments supporting this view have not dissociated rewards from the actions that lead to, or are occasioned by, them. Here, we manipulated dopamine pharmacologically and examined the effect on a task that explicitly dissociates action and reward value. We show that dopamine enhanced the neural representation of rewarding actions, without significantly affecting the representation of reward value as such. Thus, increasing dopamine levels with levodopa selectively boosted striatal and substantia nigra/ventral tegmental representations associated with actions leading to reward, but not with actions leading to the avoidance of punishment. These findings highlight a key role for dopamine in the generation of appetitively motivated actions.

Uncertainty increases pain: Evidence for a novel mechanism of pain modulation involving the periaqueductal gray

Abstract: Predictions about sensory input exert a dominant effect on what we perceive, and this is particularly true for the experience of pain. However, it remains unclear what component of prediction, from an information-theoretic perspective, controls this effect. We used a vicarious pain observation paradigm to study how the underlying statistics of predictive information modulate experience. Subjects observed judgments that a group of people made to a painful thermal stimulus, before receiving the same stimulus themselves. We show that the mean observed rating exerted a strong assimilative effect on subjective pain. In addition, we show that observed uncertainty had a specific and potent hyperalgesic effect. Using computational functional magnetic resonance imaging, we found that this effect correlated with activity in the periaqueductal gray. Our results provide evidence for a novel form of cognitive hyperalgesia relating to perceptual uncertainty, induced here by vicarious observation, with control mediated by the brainstem pain modulatory system.

Mechanisms of top-down facilitation in perception of visual objects studied by FMRI.

Abstract: Prior knowledge regarding the possible identity of an object facilitates its recognition from a degraded visual input, though the underlying mechanisms are unclear. Previous work implicated ventral visual cortex but did not disambiguate whether activity-changes in these regions are causal to or merely reflect an effect of facilitated recognition. We used functional magnetic resonance imaging to study top-down influences on processing of gradually revealed objects, by preceding each object with a name that was congruent or incongruent with the object. Congruently primed objects were recognized earlier than incongruently primed, and this was paralleled by shifts in activation profiles for ventral visual, parietal, and prefrontal cortices. Prior to recognition, defined on a trial-by-trial basis, activity in ventral visual cortex rose gradually but equivalently for congruently and incongruently primed objects. In contrast, prerecognition activity was greater with congruent priming in lateral parietal, retrosplenial, and lateral prefrontal cortices, whereas functional coupling between parietal and ventral visual (and also left lateral prefrontal and parietal) cortices was enhanced in the same context. Thus, when controlling for recognition point and stimulus information, activity in ventral visual cortex mirrors recognition success, independent of condition. Facilitation by top-down cues involves lateral parietal cortex interacting with ventral visual areas, potentially explaining why parietal lesions can lead to deficits in recognizing degraded objects even in the context of top-down knowledge.

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.