Human Functional Brain Imaging 1990–2009
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Brain Research UK Annual Review 2018/20
Brain Research UK Annual review 2018/20 Brain Research UK Welcome to our annual review Our vision is a world where everyone with a neurological One in six of us has a neurological condition. condition lives better, longer Brain Research UK is the leading dedicated funder of neurological research in the UK. We fund the best science to achieve the The brain is the most complex organ in our body. It weighs just 3lb, yet it controls our greatest impact for people affected by neurological conditions, emotions, senses and actions, every single one of them. It is how we process the to help them live better, longer. world around us. So when it breaks down, we break down. Due to a change in our reporting year, this review covers an It doesn’t have to be this way. exceptional, and busy, eighteen month period from October 2018 to March 2020 during which time we awarded grants totalling There are hundreds of neurological conditions. We fund research to discover almost £2.1 million. the causes, develop new treatments and improve the lives of all those affected. We continued our commitment to PhD and project grant funding within our three priority areas of neuro-oncology, acquired brain and spinal cord injury, and headache and facial pain. In addition, we Let’s unite to accelerate the progress of brain research. Today. awarded key funding of over £600,000 from the Graeme Watts Endowment to support Professor Linda Greensmith’s continued research into motor neurone disease at UCL Institute of Neurology. In the following pages, we feature a few stories from our many inspiring supporters. -
Biomedical Sciences 2 DRAFT 9/13/16
1 1 Report of the SEAB Task Force on Biomedical Sciences 2 DRAFT 9/13/16 3 Executive Summary 4 Progress in the biomedical sciences has crucial implications for the Nation’s health, security, 5 and competitiveness. Advances in biomedicine depend increasingly upon integrating many other 6 disciplines---most importantly, the physical and data sciences and engineering---with the 7 biological sciences. Unfortunately, the scientific responsibilities of the various federal agencies 8 are imperfectly aligned with that multidisciplinary need. Novel biomedical technologies could be 9 developed far more efficiently and strategically by enhanced inter-agency cooperation. The 10 Department of Energy’s mission-driven basic research capabilities make it an especially promising 11 partner for increased collaboration with NIH, the nation’s lead agency for biomedical research; 12 conversely, the NIH is well-positioned to expand its relationships with DOE. Particular DOE 13 capabilities of interest include instrumentation, materials, modeling and simulation, and data 14 science, which will find application in many areas of biomedical research, including cancer, 15 neurosciences, microbiology, and cell biology; the analysis of massive heterogeneous clinical and 16 genetic data; radiology and radiobiology; and biodefense. 17 To capitalize on these opportunities we recommend that the two agencies work together 18 more closely and in more strategic ways to A) define joint research programs in the most fertile 19 areas of biomedical research and applicable technologies; B) create organizational and funding 20 mechanisms that bring diverse researchers together and cross-train young people; C) secure 21 funding for one or more joint research units and/or user facilities; D) better inform OMB, 22 Congress, and the public about the importance of, and potential for, enhanced DOE-NIH 23 collaboration. -
The Use of Non-Human Primates in Research in Primates Non-Human of Use The
The use of non-human primates in research The use of non-human primates in research A working group report chaired by Sir David Weatherall FRS FMedSci Report sponsored by: Academy of Medical Sciences Medical Research Council The Royal Society Wellcome Trust 10 Carlton House Terrace 20 Park Crescent 6-9 Carlton House Terrace 215 Euston Road London, SW1Y 5AH London, W1B 1AL London, SW1Y 5AG London, NW1 2BE December 2006 December Tel: +44(0)20 7969 5288 Tel: +44(0)20 7636 5422 Tel: +44(0)20 7451 2590 Tel: +44(0)20 7611 8888 Fax: +44(0)20 7969 5298 Fax: +44(0)20 7436 6179 Fax: +44(0)20 7451 2692 Fax: +44(0)20 7611 8545 Email: E-mail: E-mail: E-mail: [email protected] [email protected] [email protected] [email protected] Web: www.acmedsci.ac.uk Web: www.mrc.ac.uk Web: www.royalsoc.ac.uk Web: www.wellcome.ac.uk December 2006 The use of non-human primates in research A working group report chaired by Sir David Weatheall FRS FMedSci December 2006 Sponsors’ statement The use of non-human primates continues to be one the most contentious areas of biological and medical research. The publication of this independent report into the scientific basis for the past, current and future role of non-human primates in research is both a necessary and timely contribution to the debate. We emphasise that members of the working group have worked independently of the four sponsoring organisations. Our organisations did not provide input into the report’s content, conclusions or recommendations. -
Generative Models, Structural Similarity, and Mental Representation
The Mind as a Predictive Modelling Engine: Generative Models, Structural Similarity, and Mental Representation Daniel George Williams Trinity Hall College University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy August 2018 The Mind as a Predictive Modelling Engine: Generative Models, Structural Similarity, and Mental Representation Daniel Williams Abstract I outline and defend a theory of mental representation based on three ideas that I extract from the work of the mid-twentieth century philosopher, psychologist, and cybernetician Kenneth Craik: first, an account of mental representation in terms of idealised models that capitalize on structural similarity to their targets; second, an appreciation of prediction as the core function of such models; and third, a regulatory understanding of brain function. I clarify and elaborate on each of these ideas, relate them to contemporary advances in neuroscience and machine learning, and favourably contrast a predictive model-based theory of mental representation with other prominent accounts of the nature, importance, and functions of mental representations in cognitive science and philosophy. For Marcella Montagnese Preface Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. -
Machine Learning for Neuroscience Geoffrey E Hinton
Hinton Neural Systems & Circuits 2011, 1:12 http://www.neuralsystemsandcircuits.com/content/1/1/12 QUESTIONS&ANSWERS Open Access Machine learning for neuroscience Geoffrey E Hinton What is machine learning? Could you provide a brief description of the Machine learning is a type of statistics that places parti- methods of machine learning? cular emphasis on the use of advanced computational Machine learning can be divided into three parts: 1) in algorithms. As computers become more powerful, and supervised learning, the aim is to predict a class label or modern experimental methods in areas such as imaging a real value from an input (classifying objects in images generate vast bodies of data, machine learning is becom- or predicting the future value of a stock are examples of ing ever more important for extracting reliable and this type of learning); 2) in unsupervised learning, the meaningful relationships and for making accurate pre- aim is to discover good features for representing the dictions. Key strands of modern machine learning grew input data; and 3) in reinforcement learning, the aim is out of attempts to understand how large numbers of to discover what action should be performed next in interconnected, more or less neuron-like elements could order to maximize the eventual payoff. learn to achieve behaviourally meaningful computations and to extract useful features from images or sound What does machine learning have to do with waves. neuroscience? By the 1990s, key approaches had converged on an Machine learning has two very different relationships to elegant framework called ‘graphical models’, explained neuroscience. As with any other science, modern in Koller and Friedman, in which the nodes of a graph machine-learning methods can be very helpful in analys- represent variables such as edges and corners in an ing the data. -
Richard L. Gregory (March 7Th 2000) ADVENTURES of a MAVERICK in the Beginning--School and War My Father Was a Scientist--An
Richard L. Gregory (March 7th 2000) ADVENTURES OF A MAVERICK In the Beginning--School and war My father was a scientist--an astronomer--being the first director of the University of London Observatory. So I was brought up with optical instruments and also with the importance of making observations. My father measured the distances of the nearer stars for most of his life-- using parallax from camera-positions separated by the 186,000,000 miles diameter of the Earthís orbit around the Sun. These measurements are crucial for scaling the universe. Is it an accident that years later I tried to scale and explain distortions of visual space? At school I learned simple electronics in our Radio Club, as we built our own short wave receivers, and then more in the RAF at Number One Signal School at Cranwell. Cranwell had excellent teaching, and was highly civilised, with its drama and music societies. I should have been posted to the Gold Coast, but a telegram recalling me from Christmas leave did not arrive in time so I was posted to Training Command in Canada. This was a year flying around the Bay of Fundy and St Johns, sometimes testing radio communications and radar; then six months with the Fleet Air Arm at Kingston, Ontario, where I had my own boat and sailed among the Thousand Islands. During almost six years in the RAF I had time to read and think on physics and biology, and wrote a science column in a local RAF magazine. I also read C.G. Jung (developing a permanent allergy) and William James (who remains a hero.) No doubt I absorbed some useful concepts from the technologies of radio and radar. -
Neuro Informatics 2015
Neuro NeuroInformatics 2015 Informatics 2015 August 20-22 Cairns,August Australia 20-22 Cairns, Australia ABSTRACT BOOK ABSTRACT BOOK Neuroinformatics 2015 8th INCF Congress Program & abstracts August 20 - 22, 2015 Cairns, Australia Neuroinformatics 2015 3 What is INCF? What is INCF? The International Neuroinformatics Coordinating Facility (INCF), together with its 18 member countries, coordinates collaborative informatics infrastructure for neuroscience and manages scientific programs to develop standards for data sharing, analysis, modeling, and simulation in order to catalyze insights into brain function in health and disease. INCF is an international organization launched in 2005, following a proposal from the Global Science Forum of the OECD to establish international coordination and collaborative informatics infrastructure for neuroscience. INCF is hosted by Karolinska Institutet and the Royal Institute of Technology, and the Secretariat is located on the Karolinska Institute Campus in Solna. INCF currently has 18 member countries across North America, Europe, Australia, and Asia. Each member country establishes an INCF National Node to further the development of Neuroinformatics and to interface with the INCF Secretariat. The mission of INCF is to share and integrate neuroscience data and knowledge worldwide, with the aim to catalyze insights into brain function in health and disease. Learn more: incf.org software.incf.org neuroinformatics2015.org INCF Member Countries as of August 2015 Victoria Node (blue), Australia Node from 1 Jan 2016 (hatched blue) 2 4 Neuroinformatics 2015 Welcome Welcome Welcome to the 8th INCF Congress in Cairns, Australia! The 8th INCF Congress on Neuroinformatics meets this year in sunny Cairns, Australia, home to the Great Barrier Reef. -
Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning Felipe Petroski Such Vashisht Madhavan Edoardo Conti Joel Lehman Kenneth O. Stanley Jeff Clune Uber AI Labs ffelipe.such, [email protected] Abstract 1. Introduction Deep artificial neural networks (DNNs) are typ- A recent trend in machine learning and AI research is that ically trained via gradient-based learning al- old algorithms work remarkably well when combined with gorithms, namely backpropagation. Evolution sufficient computing resources and data. That has been strategies (ES) can rival backprop-based algo- the story for (1) backpropagation applied to deep neu- rithms such as Q-learning and policy gradients ral networks in supervised learning tasks such as com- on challenging deep reinforcement learning (RL) puter vision (Krizhevsky et al., 2012) and voice recog- problems. However, ES can be considered a nition (Seide et al., 2011), (2) backpropagation for deep gradient-based algorithm because it performs neural networks combined with traditional reinforcement stochastic gradient descent via an operation sim- learning algorithms, such as Q-learning (Watkins & Dayan, ilar to a finite-difference approximation of the 1992; Mnih et al., 2015) or policy gradient (PG) methods gradient. That raises the question of whether (Sehnke et al., 2010; Mnih et al., 2016), and (3) evolution non-gradient-based evolutionary algorithms can strategies (ES) applied to reinforcement learning bench- work at DNN scales. Here we demonstrate they marks (Salimans et al., 2017). One common theme is can: we evolve the weights of a DNN with a sim- that all of these methods are gradient-based, including ES, ple, gradient-free, population-based genetic al- which involves a gradient approximation similar to finite gorithm (GA) and it performs well on hard deep differences (Williams, 1992; Wierstra et al., 2008; Sali- RL problems, including Atari and humanoid lo- mans et al., 2017). -
Cognitive Enhancement and the Emergence of Personhood
http://dx.doi.org/10.5007/1677-2954.2021.e80030 FROM THE NECESSITY OF BEING HUMAN TO THE POSSIBILITY OF PURSUING A GOOD LIFE: COGNITIVE ENHANCEMENT AND THE EMERGENCE OF PERSONHOOD GIOVANA LOPES1 (UNIVERSITÀ DI BOLOGNA/Italia) BRUNELLO STANCIOLI2 (UFMG/Brasil) ABSTRACT Throughout human history, cognition enhancement has not only been a constant, but also imperative for our species evolution and well-being. However, philosophical assumptions that enhancing cognition by biomedical means goes against some immutable human nature, combined with the lack of conclusive data about its use on healthy individuals and long-term effects, can sometimes result in prohibitive approaches. In this context, the authors seek to demonstrate that cognition enhancement, whether by “natural” or biotechnological means, is essential to the emergence of personhood, and to existence of an autonomous life that is guided by a person’s own conception of the good. In order to do so, the main possibilities of enhancing cognition through biotechnologies, along with its benefits and potentialities, as well as its risks and limitations, will be presented. Following Savulescu and Sandberg’s account on cognitive enhancement, it will be argued that it constitutes both a consumption good, being desirable and happiness-promoting to have well-functioning cognition, and a capital good that reduces risks, increases earning capacity, and forms a key part of human capital. Finally, having in mind that progress in the field of biotechnology aimed at cognition enhancement may improve a person’s (and society’s) well-being, the authors will argue that further research in the field is necessary, especially studies that takes into consideration dimensions such as dose, individual characteristics and task characteristics. -
Acetylcholine in Cortical Inference
Neural Networks 15 (2002) 719–730 www.elsevier.com/locate/neunet 2002 Special issue Acetylcholine in cortical inference Angela J. Yu*, Peter Dayan Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR, UK Received 5 October 2001; accepted 2 April 2002 Abstract Acetylcholine (ACh) plays an important role in a wide variety of cognitive tasks, such as perception, selective attention, associative learning, and memory. Extensive experimental and theoretical work in tasks involving learning and memory has suggested that ACh reports on unfamiliarity and controls plasticity and effective network connectivity. Based on these computational and implementational insights, we develop a theory of cholinergic modulation in perceptual inference. We propose that ACh levels reflect the uncertainty associated with top- down information, and have the effect of modulating the interaction between top-down and bottom-up processing in determining the appropriate neural representations for inputs. We illustrate our proposal by means of an hierarchical hidden Markov model, showing that cholinergic modulation of contextual information leads to appropriate perceptual inference. q 2002 Elsevier Science Ltd. All rights reserved. Keywords: Acetylcholine; Perception; Neuromodulation; Representational inference; Hidden Markov model; Attention 1. Introduction medial septum (MS), diagonal band of Broca (DBB), and nucleus basalis (NBM). Physiological studies on ACh Neuromodulators such as acetylcholine (ACh), sero- indicate that its neuromodulatory effects at the cellular tonine, dopamine, norepinephrine, and histamine play two level are diverse, causing synaptic facilitation and suppres- characteristic roles. One, most studied in vertebrate systems, sion as well as direct hyperpolarization and depolarization, concerns the control of plasticity. The other, most studied in all within the same cortical area (Kimura, Fukuda, & invertebrate systems, concerns the control of network Tsumoto, 1999). -
Model-Free Episodic Control
Model-Free Episodic Control Charles Blundell Benigno Uria Alexander Pritzel Google DeepMind Google DeepMind Google DeepMind [email protected] [email protected] [email protected] Yazhe Li Avraham Ruderman Joel Z Leibo Jack Rae Google DeepMind Google DeepMind Google DeepMind Google DeepMind [email protected] [email protected] [email protected] [email protected] Daan Wierstra Demis Hassabis Google DeepMind Google DeepMind [email protected] [email protected] Abstract State of the art deep reinforcement learning algorithms take many millions of inter- actions to attain human-level performance. Humans, on the other hand, can very quickly exploit highly rewarding nuances of an environment upon first discovery. In the brain, such rapid learning is thought to depend on the hippocampus and its capacity for episodic memory. Here we investigate whether a simple model of hippocampal episodic control can learn to solve difficult sequential decision- making tasks. We demonstrate that it not only attains a highly rewarding strategy significantly faster than state-of-the-art deep reinforcement learning algorithms, but also achieves a higher overall reward on some of the more challenging domains. 1 Introduction Deep reinforcement learning has recently achieved notable successes in a variety of domains [23, 32]. However, it is very data inefficient. For example, in the domain of Atari games [2], deep Reinforcement Learning (RL) systems typically require tens of millions of interactions with the game emulator, amounting to hundreds of hours of game play, to achieve human-level performance. As pointed out by [13], humans learn to play these games much faster. This paper addresses the question of how to emulate such fast learning abilities in a machine—without any domain-specific prior knowledge. -
TREVOR W. ROBBINS, CBE FRS Fmedsci Fbpss
TREVOR W. ROBBINS, CBE FRS FMedSci FBPsS Professor of Cognitive Neuroscience and Experimental Psychology Trevor Robbins is accepting applications for PhD students. Email: [email protected] Office Phone: +44 (0)1223 (3)33551 Websites: • http://www.neuroscience.cam.ac.uk/directory/profile.php?Trevor Download as vCard Biography: Trevor Robbins was appointed in 1997 as the Professor of Cognitive Neuroscience at the University of Cambridge. He was elected to the Chair of Experimental Psychology (and Head of Department) at Cambridge from October 2002. He is also Director of the Behavioural and Clinical Neuroscience Institute (BCNI), jointly funded by the Medical Research Council and the Wellcome Trust. The mission of the BCNI is to inter-relate basic and clinical research in psychiatry and neurology for such conditions as Parkinson's, Huntington's, and Alzheimer's diseases, frontal lobe injury, schizophrenia, depression, drug addiction and developmental syndromes such as attention deficit/hyperactivity disorder. Trevor is a Fellow of the British Psychological Society (1990), the Academy of Medical Sciences (2000) and the Royal Society (2005). He has been President of the European Behavioural Pharmacology Society (1992-1994) and he won that Society's inaugural Distinguished Scientist Award in 2001. He was also President of the British Association of Psychopharmacology from 1996 to 1997. He has edited the journal Psychopharmacology since 1980 and joined the editorial board of Science in January 2003. He has been a member of the Medical Research Council (UK) and chaired the Neuroscience and Mental Health Board from 1995 until 1999. He has been included on a list of the 100 most cited neuroscientists by ISI, has published over 600 full papers in scientific journals and has co-edited seven books (Psychology for Medicine: The Prefrontal Cortex; Executive and Cognitive Function: Disorders of Brain and Mind 2:Drugs and the Future: The Neurobiology of Addiction; New Vistas.