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.

Voxel-based morphometry reveals reduced grey matter volume in the temporal cortex of developmental prosopagnosics

Abstract: Individuals with developmental prosopagnosia exhibit severe and lasting difficulties in recognizing faces despite the absence of apparent brain abnormalities. We used voxel-based morphometry to investigate whether developmental prosopagnosics show subtle neuroanatomical differences from controls. An analysis based on segmentation of T1-weighted images from 17 developmental prosopagnosics and 18 matched controls revealed that they had reduced grey matter volume in the right anterior inferior temporal lobe and in the superior temporal sulcus/middle temporal gyrus bilaterally. In addition, a voxel-based morphometry analysis based on the segmentation of magnetization transfer parameter maps showed that developmental prosopagnosics also had reduced grey matter volume in the right middle fusiform gyrus and the inferior temporal gyrus. Multiple regression analyses relating three distinct behavioural component scores, derived from a principal component analysis, to grey matter volume revealed an association between a component related to facial identity and grey matter volume in the left superior temporal sulcus/middle temporal gyrus plus the right middle fusiform gyrus/inferior temporal gyrus. Grey matter volume in the lateral occipital cortex was associated with component scores related to object recognition tasks. Our results demonstrate that developmental prosopagnosics have reduced grey matter volume in several regions known to respond selectively to faces and provide new evidence that integrity of these areas relates to face recognition ability.

Pupillary contagion: central mechanisms engaged in sadness processing

Abstract: Empathic responses underlie our ability to share emotions and sensations with others. We investigated whether observed pupil size modulates our perception of other’s emotional expressions and examined the central mechanisms modulated by incidental perception of pupil size in emotional facial expressions. We show that diminishing pupil size enhances ratings of emotional intensity and valence for sad, but not happy, angry or neutral facial expressions. This effect was associated with modulation of neural activity within cortical and subcortical regions implicated in social cognition. In an identical context, we show that the observed pupil size was mirrored by the observers’ own pupil size. This empathetic contagion engaged the brainstem pupillary control nuclei (Edinger–Westphal) in proportion to individual subject’s sensitivity to this effect. These findings provide evidence that perception–action mechanisms extend to non-volitional operations of the autonomic nervous system.

Dopamine Modulates Episodic Memory Persistence in Old Age

Abstract: Activation of the hippocampus is required to encode memories for new events (or episodes). Observations from animal studies suggest that, for these memories to persist beyond 4-6 h, a release of dopamine generated by strong hippocampal activation is needed. This predicts that dopaminergic enhancement should improve human episodic memory persistence also for events encoded with weak hippocampal activation. Here, using pharmacological functional MRI (fMRI) in an elderly population in which there is a loss of dopamine neurons as part of normal aging, we show this very effect. The dopamine precursor levodopa led to a dose-dependent (inverted U-shape) persistent episodic memory benefit for images of scenes when tested after 6 h, independent of whether encoding-related hippocampal fMRI activity was weak or strong (U-shaped dose-response relationship). This lasting improvement even for weakly encoded events supports a role for dopamine in human episodic memory consolidation, albeit operating within a narrow dose range.

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.