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.

Parsing the role of the hippocampus in approach-avoidance conflict

Abstract: The hippocampus plays a central role in the approach-avoidance conflict that is central to the genesis of anxiety. However, its exact functional contribution has yet to be identified. We designed a novel gambling task that generated approach-avoidance conflict while controlling for spatial processing. We fit subjects' behavior using a model that quantified the subjective values of choice options, and recorded neural signals using functional magnetic resonance imaging (fMRI). Distinct functional signals were observed in anterior hippocampus, with inferior hippocampus selectively recruited when subjects rejected a gamble, to a degree that covaried with individual differences in anxiety. The superior anterior hippocampus, in contrast, uniquely demonstrated value signals that were potentiated in the context of approach-avoidance conflict. These results implicate the anterior hippocampus in behavioral avoidance and choice monitoring, in a manner relevant to understanding its role in anxiety. Our findings highlight interactions between subregions of the hippocampus as an important focus for future study.

The Origins of Forgetting in a Case of Isolated Retrograde Amnesia Following a Haemorrhage: Evidence From Functional Imaging

Abstract: Following a left superior dorsolateral prefrontal haemorrhage, a man in his 40s presented with a dense isolated retrograde amnesia for 19 years preceding the stroke. This period of his lif...

The effect of motivation on movement: a study of bradykinesia in Parkinson's disease.

Abstract: Bradykinesia is a cardinal feature of Parkinson's disease (PD). Despite its disabling impact, the precise cause of this symptom remains elusive. Recent thinking suggests that bradykinesia may be more than simply a manifestation of motor slowness, and may in part reflect a specific deficit in the operation of motivational vigour in the striatum. In this paper we test the hypothesis that movement time in PD can be modulated by the specific nature of the motivational salience of possible action-outcomes.

Reward-Related Activity in Ventral Striatum Is Action Contingent and Modulated by Behavioral Relevance

Abstract: Multiple features of the environment are often imbued with motivational significance, and the relative importance of these can change across contexts. The ability to flexibly adjust evaluative processes so that currently important features of the environment alone drive behavior is critical to adaptive routines. We know relatively little about the neural mechanisms involved, including whether motivationally significant features are obligatorily evaluated or whether current relevance gates access to value-sensitive regions. We addressed these questions using functional magnetic resonance imaging data and a task design where human subjects had to choose whether to accept or reject an offer indicated by visual and auditory stimuli. By manipulating, on a trial-by-trial basis, which stimulus determined the value of the offer, we show choice activity in the ventral striatum solely reflects the value of the currently relevant stimulus, consistent with a model wherein behavioral relevance modulates the impact of sensory stimuli on value processing. Choice outcome signals in this same region covaried positively with wins on accept trials, and negatively with wins on reject trials, consistent with striatal activity at feedback reflecting correctness of response rather than reward processing per se. We conclude that ventral striatum activity during decision making is dynamically modulated by behavioral context, indexed here by task relevance and action selection.

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.