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.

Functional heterogeneity in human olfactory cortex: An event-related functional magnetic resonance imaging study

Abstract: Studies of patients with focal brain injury indicate that smell perception involves caudal orbitofrontal and medial temporal cortices, but a more precise functional organization has not been characterized. In addition, although it is believed that odors are potent triggers of emotion, support for an anatomical association is scant. We sought to define the neural substrates of human olfactory information processing and determine how these are modulated by affective properties of odors. We used event-related functional magnetic resonance imaging (fMRI) in an olfactory version of a classical conditioning paradigm, whereby neutral faces were paired with pleasant, neutral, or unpleasant odors, under 50% reinforcement. By comparing paired (odor/face) and unpaired (face only) conditions, odor-evoked neural activations could be isolated specifically. In primary olfactory (piriform) cortex, spatially and temporally dissociable responses were identified along a rostrocaudal axis. A nonhabituating response in posterior piriform cortex was tuned to all odors, whereas activity in anterior piriform cortex reflected sensitivity to odor affect. Bilateral amygdala activation was elicited by all odors, regardless of valence. In posterior orbitofrontal cortex, neural responses evoked by pleasant and unpleasant odors were segregated within medial and lateral segments, respectively. The results indicate functional heterogeneity in areas critical to human olfaction. They also show that brain regions mediating emotional processing are differentially activated by odor valence, providing evidence for a close anatomical coupling between olfactory and emotional processes.

Threatening a rubber hand that you feel is yours elicits a cortical anxiety response

Abstract: The feeling of body ownership is a fundamental aspect of self-consciousness. The underlying neural mechanisms can be studied by using the illusion where a person is made to feel that a rubber hand is his or her own hand by brushing the person's hidden real hand and synchronously brushing the artificial hand that is in full view. Here we show that threat to the rubber hand can induce a similar level of activity in the brain areas associated with anxiety and interoceptive awareness (insula and anterior cingulate cortex) as when the person's real hand is threatened. We further show that the stronger the feeling of ownership of the artificial hand, the stronger the threat-evoked neuronal responses in the areas reflecting anxiety. Furthermore, across subjects, activity in multisensory areas reflecting ownership predicted the activity in the interoceptive system when the hand was under threat. Finally, we show that there is activity in medial wall motor areas, reflecting an urge to withdraw the artificial hand when it is under threat. These findings suggest that artificial limbs can evoke the same feelings as real limbs and provide objective neurophysiological evidence that the rubber hand is fully incorporated into the body. These findings are of fundamental importance because they suggest that the feeling of body ownership is associated with changes in the interoceptive systems.

Prefrontal Contributions to Metacognition in Perceptual Decision Making

Abstract: Neuroscience has made considerable progress in understanding the neural substrates supporting cognitive performance in a number of domains, including memory, perception, and decision making. In contrast, how the human brain generates metacognitive awareness of task performance remains unclear. Here, we address this question by asking participants to perform perceptual decisions while providing concurrent metacognitive reports during fMRI scanning. We show that activity in right rostrolateral prefrontal cortex (rlPFC) satisfies three constraints for a role in metacognitive aspects of decision-making. Right rlPFC showed greater activity during self-report compared to a matched control condition, activity in this region correlated with reported confidence, and the strength of the relationship between activity and confidence predicted metacognitive ability across individuals. In addition, functional connectivity between right rlPFC and both contralateral PFC and visual cortex increased during metacognitive reports. We discuss these findings in a theoretical framework where rlPFC re-represents object-level decision uncertainty to facilitate metacognitive report.

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.