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.

A neuromodulatory role for the human amygdala in processing emotional facial expressions

Abstract: Localized amygdalar lesions in humans produce deficits in the recognition of fearful facial expressions. We used functional neuroimaging to test two hypotheses: (i) that the amygdala and some of its functionally connected structures mediate specific neural responses to fearful expressions; (ii) that the early visual processing of emotional faces can be influenced by amygdalar activity. Normal subjects were scanned using PET while they performed a gender discrimination task involving static grey-scale images of faces expressing varying degrees of fear or happiness. In support of the first hypothesis, enhanced activity in the left amygdala, left pulvinar, left anterior insula and bilateral anterior cingulate gyri was observed during the processing of fearful faces. Evidence consistent with the second hypothesis was obtained by a demonstration that amygdalar responses predict expression-specific neural activity in extrastriate cortex.

A subcortical pathway to the right amygdala mediating “unseen” fear

Abstract: Neuroimaging studies have shown differential amygdala responses to masked (“unseen”) emotional stimuli. How visual signals related to such unseen stimuli access the amygdala is unknown. A possible pathway, involving the superior colliculus and pulvinar, is suggested by observations of patients with striate cortex lesions who show preserved abilities to localize and discriminate visual stimuli that are not consciously perceived (“blindsight”). We used measures of right amygdala neural activity acquired from volunteer subjects viewing masked fear-conditioned faces to determine whether a colliculo-pulvinar pathway was engaged during processing of these unseen target stimuli. Increased connectivity between right amygdala, pulvinar, and superior colliculus was evident when fear-conditioned faces were unseen rather than seen. Right amygdala connectivity with fusiform and orbitofrontal cortices decreased in the same condition. By contrast, the left amygdala, whose activity did not discriminate seen and unseen fear-conditioned targets, showed no masking-dependent changes in connectivity with superior colliculus or pulvinar. These results suggest that a subcortical pathway to the right amygdala, via midbrain and thalamus, provides a route for processing behaviorally relevant unseen visual events in parallel to a cortical route necessary for conscious identification.

Dissociable neural responses to facial expressions of sadness and anger

Abstract: Previous neuroimaging and neuropsychological studies have investigated the neural substrates which mediate responses to fearful, disgusted and happy expressions. No previous studies have investigated the neural substrates which mediate responses to sad and angry expressions. Using functional neuroimaging, we tested two hypotheses. First, we tested whether the amygdala has a neural response to sad and/or angry facial expressions. Secondly, we tested whether the orbitofrontal cortex has a specific neural response to angry facial expressions. Volunteer subjects were scanned, using PET, while they performed a sex discrimination task involving static grey-scale images of faces expressing varying degrees of sadness and anger. We found that increasing intensity of sad facial expression was associated with enhanced activity in the left amygdala and right temporal pole. In addition, we found that increasing intensity of angry facial expression was associated with enhanced activity in the orbitofrontal and anterior cingulate cortex. We found no support for the suggestion that angry expressions generate a signal in the amygdala. The results provide evidence for dissociable, but interlocking, systems for the processing of distinct categories of negative facial expression.

Encoding predictive reward value in human amygdala and orbitofrontal cortex

Abstract: Adaptive behavior is optimized in organisms that maintain flexible representations of the value of sensory-predictive cues. To identify central representations of predictive reward value in humans, we used reinforcer devaluation while measuring neural activity with functional magnetic resonance imaging. We presented two arbitrary visual stimuli, both before and after olfactory devaluation, in a paradigm of appetitive conditioning. In amygdala and orbitofrontal cortex, responses evoked by a predictive target stimulus were decreased after devaluation, whereas responses to the nondevalued stimulus were maintained. Thus, differential activity in amygdala and orbitofrontal cortex encodes the current value of reward representations accessible to predictive cues.

Human cingulate cortex and autonomic control: converging neuroimaging and clinical evidence

Abstract: Human anterior cingulate function has been explained primarily within a cognitive framework. We used functional MRI experiments with simultaneous electrocardiography to examine regional brain activity associated with autonomic cardiovascular control during performance of cognitive and motor tasks. Using indices of heart rate variability, and high- and low-frequency power in the cardiac rhythm, we observed activity in the dorsal anterior cingulate cortex (ACC) related to sympathetic modulation of heart rate that was dissociable from cognitive and motor-related activity. The findings predict that during effortful cognitive and motor behaviour the dorsal ACC supports the generation of associated autonomic states of cardiovascular arousal. We subsequently tested this prediction by studying three patients with focal damage involving the ACC while they performed effortful cognitive and motor tests. Each showed abnormalities in autonomic cardiovascular responses with blunted autonomic arousal to mental stress when compared with 147 normal subjects tested in identical fashion. Thus, converging neuroimaging and clinical findings suggest that ACC function mediates context-driven modulation of bodily arousal states.

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.