Nathaniel D. Daw

Bio: Nathaniel D. Daw is an academic researcher from Princeton University. The author has contributed to research in topics: Reinforcement learning & Cognition. The author has an hindex of 71, co-authored 209 publications receiving 23094 citations. Previous affiliations of Nathaniel D. Daw include University of Cambridge & University of Melbourne.

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

Abstract: A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.

Cortical substrates for exploratory decisions in humans

Abstract: Humans are remarkably curious, and that is useful in helping us to learn about new environments and possibilities. But curiosity killed the cat, they say, and it also carries with it substantial potential risks and costs for us. Statisticians, engineers and economists have long considered ways of balancing the costs and benefits of exploration. Tests involving a gambling task and an fMRI brain scanner now show that humans appear to obey similar principles when considering their options. The players had to balance the desire to select the richest option based on accumulated experience against the desire to choose a less familiar option that might have a larger payoff. The frontopolar cortex, a brain area known to be involved in cognitive control, was preferentially active during exploratory decisions. The results suggest a neurobiological account of human exploration and point to a new area for behavioural and neural investigations.

The importance of mixed selectivity in complex cognitive tasks

Abstract: Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input–output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons. Neurophysiology experiments in behaving animals are often analysed and modelled with a reverse engineering perspective, with the more or less explicit intention to identify highly specialized components with distinct functional roles in the behaviour under study. Physiologists often find the components they are looking for, contributing to the understanding of the functions and the underlying mechanisms of various brain areas, but they are also bewildered by numerous observations that are difficult to interpret. Many cells, especially in higherorder brain structures like the prefrontal cortex (PFC), often have complex and diverse response properties that are not organized anatomically, and that simultaneously reflect different parameters. These neurons are said to have mixed selectivity to multiple aspects of the task. For instance, in rule-based sensory-motor mapping tasks (such as the Wisconsin card sorting test), the response of a PFC cell may be correlated with parameters of the sensory stimuli, task rule, motor response or any combination of these 1,2 . The predominance of these mixed selectivity neurons seems to be a hallmark of PFC and other brain structures involved in cognition. Understanding such neural representations has been a major conceptual challenge in the field. To characterize the statistics and understand the functional role of mixed selectivity, we analysed neural activity recorded in the PFC of monkeys during a sequence memory task 3 . Motivated by recent theoretical advances in understanding how machine learning principles play out in the functioning of neuronal circuits 4–10 , we devised a new analysis of the recorded population activity. This analysis revealed that the observed mixed selectivity can be naturally understood as a signature of the information-encoding strategy of state-of-the-art classifiers like support vector machines 11 . Specifically we found that (1) the populations of recorded neurons encode distributed information that is not contained in classical selectivity to individual task aspects, (2) the recorded neural representations are high-dimensional, and (3) the dimensionality of the recorded neural representations predicts behavioural performance.

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.).

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Abstract: Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.