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.

From Threat to Fear: The Neural Organization of Defensive Fear Systems in Humans

Abstract: Postencounter and circa-strike defensive contexts represent two adaptive responses to potential and imminent danger. In the context of a predator, the postencounter reflects the initial detection of the potential threat, whereas the circa-strike is associated with direct predatory attack. We used functional magnetic resonance imaging to investigate the neural organization of anticipation and avoidance of artificial predators with high or low probability of capturing the subject across analogous postencounter and circa-strike contexts of threat. Consistent with defense systems models, postencounter threat elicited activity in forebrain areas, including subgenual anterior cingulate cortex (sgACC), hippocampus, and amygdala. Conversely, active avoidance during circa-strike threat increased activity in mid-dorsal ACC and midbrain areas. During the circa-strike condition, subjects showed increased coupling between the midbrain and mid-dorsal ACC and decreased coupling with the sgACC, amygdala, and hippocampus. Greater activity was observed in the right pregenual ACC for high compared with low probability of capture during circa-strike threat. This region showed decreased coupling with the amygdala, insula, and ventromedial prefrontal cortex. Finally, we found that locomotor errors correlated with subjective reports of panic for the high compared with low probability of capture during the circa-strike threat, and these panic-related locomotor errors were correlated with midbrain activity. These findings support models suggesting that higher forebrain areas are involved in early-threat responses, including the assignment and control of fear, whereas imminent danger results in fast, likely "hard-wired," defensive reactions mediated by the midbrain.

The functional roles of prefrontal cortex in episodic memory. I. Encoding.

Abstract: Functional neuroimaging studies of episodic memory consistently report an association between memory encoding operations and left prefrontal cortex (PFC) activation. Encoding-related activation has been described in dorsolateral, ventrolateral and anterior prefrontal regions. We tested the hypothesis that a specific component of this left PFC activation reflects organizational processes necessary for optimal memory encoding. Subjects underwent PET scans while learning auditorily presented word lists under dual task conditions. The degree to which they were required to organize word lists semantically was systematically varied across three experimental conditions. A task in which words were already organized produced the least degree of left PFC activity whereas a task requiring subjects to generate an organizational structure was associated with maximal activity in this region. This activation was localized to a region just above the inferior frontal sulcus. The functional specificity of this increased activity for organizational processes was tested using a concurrent distracting task known to disrupt these processes. Distraction resulted in a significant attenuation of this activation during the task emphasizing organizational processes but not other encoding tasks. In contrast, the distraction task resulted in reduced activity in a more ventral/anterior PFC region expressed equally for all memory tasks. The findings indicate that a key function of left dorsolateral PFC at encoding relates specifically to the use of executive processes necessary for the creation of an organizational structure. Activity in more ventral and anterior left PFC regions would appear to reflect a less specific component of episodic memory encoding.

Disentangling the Roles of Approach, Activation and Valence in Instrumental and Pavlovian Responding

Abstract: Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.

The functional roles of prefrontal cortex in episodic memory

Abstract: Summary Functional neuroimaging studies of episodic memory consistently report an association between memory encoding operations and left prefrontal cortex (PFC) activation. Encoding-related activation has been described in dorsolateral, ventrolateral and anterior prefrontal regions. We tested the hypothesis that a specific component of this left PFC activation reflects organizational processes necessary for optimal memory encoding. Subjects underwent PET scans while learning auditorily presented word lists under dual task conditions. The degree to which they were required to organize word lists semantically was systematically varied across three experimental conditions. A task in which words were already organized produced the least degree of left PFC activity whereas a task requiring subjects to generate an organizational structure was associated with maximal activity in this

Dopaminergic modulation of impaired cognitive activation in the anterior cingulate cortex in schizophrenia.

Abstract: DOPAMINERGIC dysregulation remains an empirical cornerstone for theories concerning the causation of schizophrenia. Evidence for a dopamine system dysfunction in schizophrenia includes the psychosis-inducing effects of dopaminergic agonists1,2 and the antipsychotic potency of dopaminergic antagonists3,4. Here we use positron emission tomography (PET) to examine the regulatory role of dopamine on cortical function in normal subjects and unmedicated schizophrenic patients. Using a factorial experimental design, we compared the effect of dopaminergic manipulation with apomorphine on a neural response to a cognitive task. In the schizophrenic patients, relative to controls, an impaired cognitive activation of the anterior cingulate cortex was significantly modulated by a manipulation of dopaminergic neurotransmission. Thus, after apomorphine, the schizophrenic subjects displayed a significantly enhanced cognitive activation of the anterior cingulate cortex relative to the controls. These data provide in vivo evidence that an impaired cognitive-task-induced activation of the anterior cingulate cortex in schizophrenic patients can be significantly modulated by a dopaminergic manipulation.

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.