Valentino Braitenberg, Neuroanatomy, and Brain Function
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Song Exposure Affects HVC Ultrastructure in Juvenile Zebra Finches
Song Exposure affects HVC Ultrastructure in Juvenile Zebra Finches Houda G Khaled Advisor: Dr. Sharon M.H. Gobes Department of Neuroscience Submitted in Partial Fulfillment of the Prerequisite for Honors in Biochemistry May 2016 © 2016 Houda Khaled Table of Contents ABSTRACT ........................................................................................................................................................ 2 ACKNOWLEDGEMENTS .............................................................................................................................. 3 INTRODUCTION ............................................................................................................................................. 4 I. SYNAPTIC PLASTICITY IN LEARNING AND MEMORY ................................................................................ 4 Basic principles of synapse structure ............................................................................................................. 4 Changes in synapse number, size and shape ................................................................................................ 6 II. SONG ACQUISITION AS A LEARNING PARADIGM .................................................................................. 7 The song system ................................................................................................................................................. 9 Structural synaptic plasticity in the song system ..................................................................................... -
Neuro Informatics 2020
Neuro Informatics 2019 September 1-2 Warsaw, Poland PROGRAM BOOK What is INCF? About INCF 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 in Stockholm, Sweden. INCF currently has Governing and Associate Nodes spanning 4 continents, with an extended network comprising organizations, individual researchers, industry, and publishers. INCF promotes the implementation of neuroinformatics and aims to advance data reuse and reproducibility in global brain research by: • developing and endorsing community standards and best practices • leading the development and provision of training and educational resources in neuroinformatics • promoting open science and the sharing of data and other resources • partnering with international stakeholders to promote neuroinformatics at global, national and local levels • engaging scientific, clinical, technical, industry, and funding partners in colla- borative, community-driven projects INCF supports the FAIR (Findable Accessible Interoperable Reusable) principles, and strives to implement them across all deliverables and activities. Learn more: incf.org neuroinformatics2019.org 2 Welcome Welcome to the 12th INCF Congress in Warsaw! It makes me very happy that a decade after the 2nd INCF Congress in Plzen, Czech Republic took place, for the second time in Central Europe, the meeting comes to Warsaw. The global neuroinformatics scenery has changed dramatically over these years. With the European Human Brain Project, the US BRAIN Initiative, Japanese Brain/ MINDS initiative, and many others world-wide, neuroinformatics is alive more than ever, responding to the demands set forth by the modern brain studies. -
Interplay of Multiple Plasticities and Activity Dependent Rules: Data, Models and Possible Impact on Learning
Interplay of multiple plasticities and activity dependent rules: data, models and possible impact on learning Gaetan Vignoud, Alexandre Mendes, Sylvie Perez, Jonathan Touboul*, Laurent Venance* (* equal contributions, email: fi[email protected]) College` de France, Center for Interdisciplinary Research in Biology, 11 Place Marcelin Berthelot, 75005 Paris, France Abstract the millisecond timing of the paired activities on either side of Synaptic plasticity, the activity-dependent evolution neuronal the synapse (Feldman, 2012). As such, plasticity in the stria- connectivity, is admitted to underpin learning and memory. A tum has been widely studied and a variety of spike-timing de- paradigmatic plasticity depending on the spike timings of cells pendent plasticity were observed in response to a large num- on both sides of the synapse (STDP), was experimentally ev- ber of presentations of a fixed pre- and post-synaptic stim- idenced through multiple repetitions of fixed pre- and post- ulation pattern. Those plasticities were attributed to a di- synaptic spike patterns. Theoretical and experimental com- verse neurotransmitters and pathways including for instance munities often implicitly admit that plasticity is gradually estab- endocannabinoids (eCB), NMDA receptors, AMPA receptors lished as stimulus patterns are presented. Here, we evaluate or voltage-sensitive calcium channels. Moreover, the vast ma- this hypothesis experimentally and theoretically in the stria- jority of the studies focused on cortical inputs, and much -
Intelligence Without Reason
Intelligence Without Reason Rodney A. Brooks MIT Artificial Intelligence Lab 545 Technology Square Cambridge, MA 02139, USA Abstract certain modus operandi over the years, which includes a particular set of conventions on how the inputs and out- Computers and Thought are the two categories puts to thought and reasoning are to be handled (e.g., that together define Artificial Intelligence as a the subfield of knowledge representation), and the sorts discipline. It is generally accepted that work in of things that thought and reasoning do (e.g,, planning, Artificial Intelligence over the last thirty years problem solving, etc.). 1 will argue that these conven has had a strong influence on aspects of com- tions cannot account for large aspects of what goes into puter architectures. In this paper we also make intelligence. Furthermore, without those aspects the va the converse claim; that the state of computer lidity of the traditional Artificial Intelligence approaches architecture has been a strong influence on our comes into question. I will also argue that much of the models of thought. The Von Neumann model of landmark work on thought has been influenced by the computation has lead Artificial Intelligence in technological constraints of the available computers, and particular directions. Intelligence in biological thereafter these consequences have often mistakenly be systems is completely different. Recent work in come enshrined as principles, long after the original im behavior-based Artificial Intelligence has pro petus has disappeared. duced new models of intelligence that are much closer in spirit to biological systems. The non- From an evolutionary stance, human level intelligence Von Neumann computational models they use did not suddenly leap onto the scene. -
Neurorobotics Lecture
Neurorobotics An introduction Marc-Oliver Gewaltig In this lecture you’ll learn 1. What is Neurorobotics 2. Examples of simple neurorobots 1. attraction and avoidance 2. reflexes vs. learned behavior 3. The sensory-motor loop 4. Learning in neurorobotics 1. unsupervised learning for sensory representations 2. reinforcement learning for action learning What is Neurorobotics Neurorobotics, is the combined study of neuroscience, robotics, and artificial intelligence. It is the science and technology of embodied autonomous neural systems. https://en.wikipedia.org/wiki/Neurorobotics Neurorobotics: Embodied in silico neuroscience Spinal Cord Reconstructed Reflexes spinal cord/ CDPs brain models Embodiment and virtual environments Musculo-skeletal system – compliant actuators and mechanics Starting simple: Valentino Braitenberg’s Vehicles Valentino Braitenberg (1926-2011) Braitenberg, V. (1984). Vehicles: Experiments in Photo: Alfred Wegener, commons.wikimedia.org synthetic psychology. Cambridge, MA: MIT Press. Vehicle 1 1 Vehicle 2a 1 2a Vehicle 2b 1 2a 2b Vehicle 3 1 2a 2b 3 Vehicle 3 1 2a 2b 3 Exercise How will vehicle 3 move? Generalizing the Braitenberg vehicle Exercise Using weights in {-1,+1}, which weight configurations implement the vehicles 2a, 2b, and 2c? speed light Biological and Non-biological bodies Sensors: cameras, microphones, etc Artificial brain with neurons Servo motors with wheels Biological and Non-biological bodies Sensors: cameras, microphones, etc encode Artificial brain with neurons Servo motors with wheels Biological and Non-biological bodies Sensors: cameras, microphones, etc encode Artificial brain with neurons Servo motors with wheels decode Perception Action Vision Behaviors Hearing Smell Action Central pattern generators Touch Perception Reflexes Temperature Vestibular Muscle contraction Proprioception Perception Short-term Long-term Action memory memory Drives & Working Vision Cognitive Motivation memory control Action Behaviors Sensor Reward & selection Hearing fusion punish. -
A Theory of Consumer Fraud in Market Economies
A THEORY OF FRAUD IN MARKET ECONOMIES ADISSERTATIONIN Economics and Social Science Presented to the Faculty of the University of Missouri-Kansas City in partial fulfillment of the requirements for the degree DOCTOR OF PHILOSOPHY by Nicola R. Matthews B.F.A, School of Visual Arts, 2002 M.A., State University College Bu↵alo, 2009 Kansas City, Missouri 2018 ©2018 Nicola R. Matthews All Rights Reserved A THEORY OF FRAUD IN MARKET ECONOMIES Nicola R. Matthews, Candidate for the Doctor of Philosophy Degree University of Missouri-Kansas City, 2018 ABSTRACT Of the many forms of human behavior, perhaps none more than those of force and fraud have caused so much harm in both social and ecological environments. In spite of this, neither shows signs of weakening given their current level of toleration. Nonetheless, what can be said in respect to the use of force in the West is that it has lost most of its competitiveness. While competitive force ruled in the preceding epoch, this category of violence has now been reduced to a relatively negligible degree. On the other hand, the same cannot be said of fraud. In fact, it appears that it has moved in the other direction and become more prevalent. The causes for this movement will be the fundamental question directing the inquiry. In the process, this dissertation will trace historical events and methods of control ranging from the use of direct force, to the use of ceremonies and rituals and to the use of the methods of law. An additional analysis of transformation will also be undertaken with regards to risk-sharing-agrarian-based societies to individual-risk-factory-based ones. -
Boden Grey Walter's
Grey Walter’s Anticipatory Tortoises Margaret Boden Grey Walter and the Ratio Club The British physiologist William Grey Walter (1910–1977) was an early member of the interdisciplinary Ratio Club. This was a small dining club that met several times a year from 1949 to 1955, with a nostalgic final meeting in 1958, at London’s National Hospital for Neurological Diseases. The founder-secretary was the neurosurgeon John Bates, who had worked (alongside the psychologist Kenneth Craik) on servomechanisms for gun turrets during the war. The club was a pioneering source of ideas in what Norbert Wiener had recently dubbed ‘cybernetics’.1 Indeed, Bates’ archive shows that the letter inviting membership spoke of ‘people who had Wiener’s ideas before Wiener’s book appeared’.2 In fact, its founders had considered calling it the Craik Club, in memory of Craik’s work—not least, his stress on ‘synthetic’ models of psychological theories.3 In short, the club was the nucleus of a thriving British tradition of cybernetics, started independently of the transatlantic version. The Ratio members—about twenty at any given time—were a very carefully chosen group. Several of them had been involved in wartime signals research or intelligence work at Bletchley Park, where Alan Turing had used primitive computers to decipher the Nazis’ Enigma code.4 They were drawn from a wide range of disciplines: clinical psychiatry and neurology, physiology, neuroanatomy, mathematics/statistics, physics, astrophysics, and the new areas of control engineering and computer science.5 The aim was to discuss novel ideas: their own, and those of guests—such as Warren McCulloch. -
Arxiv:1711.09601V4 [Cs.CV] 5 Oct 2018 Unlabeled Data Towards What the Network Needs (Not) to Forget, Which May Vary Depending on Test Conditions
Memory Aware Synapses: Learning what (not) to forget Rahaf Aljundi1, Francesca Babiloni1, Mohamed Elhoseiny2, Marcus Rohrbach2, and Tinne Tuytelaars1 1 KU Leuven, ESAT-PSI, iMEC, Belgium 2 Facebook AI Research Abstract. Humans can learn in a continuous manner. Old rarely utilized knowl- edge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowl- edge has to be preserved or erased selectively. Inspired by neuroplasticity, we propose a novel approach for lifelong learning, coined Memory Aware Synapses (MAS). It computes the importance of the parameters of a neural network in an unsupervised and online manner. Given a new sample which is fed to the net- work, MAS accumulates an importance measure for each parameter of the net- work, based on how sensitive the predicted output function is to a change in this parameter. When learning a new task, changes to important parameters can then be penalized, effectively preventing important knowledge related to previous tasks from being overwritten. Further, we show an interesting connection between a local version of our method and Hebb’s rule, which is a model for the learning process in the brain. We test our method on a sequence of object recognition tasks and on the challenging problem of learning an embedding for predicting <subject, predicate, object> triplets. We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on arXiv:1711.09601v4 [cs.CV] 5 Oct 2018 unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions. -
Was a Neurologist with a Vast Knowledge Including a Solid Scientific and Engineering Training
Scientiae Mathematicae Japonicae Online, e-2006, 861–863 861 ON LAWRENCE STARK AND BIOMEDICAL ENGINEERING Shiro Usui Received March 27, 2006 Abstract. We provide a brief overview of Lawrence Stark’s fundamental contribu- tions to Biomedical Engineering. Lawrence Stark (1926–2004) was a neurologist with a vast knowledge including a solid scientific and engineering training. He pioneered in what is nowadays sometimes called Biomedical Engineering, with an emphasis on the quantitative description of human brain control of movement and vision. In this note, I would like to outline some of his main achievements. Further information may be found, e.g., in [1, 2, 3] and references therein. Stark was interested in science since early childhood. He obtained B.A. at Columbia in 1945, where he majored in English, biology and zoology in only one year and a half. During the war he enrolled in the Navy, who sent him to Albany Medical College, where he earned MD in 1948. He was later awarded Sc.D.h.c. at SUNY in 1988 and Ph.D.h.c. at Tokushima University, Japan, in 1992. He received the Morlock Award in Biomedical Engineering in 1977, and the Franklin V. Taylor Award, Systems Man and Cybernetics Society in 1989. Lawrence Stark held faculty positions at Yale, 1954–1960, as an Assistant Professor of Medicine (Neurology) and Associate Physician; at Massachusetts Institute of Technology, 1960–1965, as Head of the Neurology Section of the Center for Communication Sciences, Electronic Systems Laboratory and Research Laboratory for Electronics; at the University of Illinois at Chicago Circle, 1965–1968, as Professor of Bioengineering, Neurology and Physiology and Chairman of the Biomedical Engineering Department at the Presbyterian St. -
Building a “Cross-Roads Discipline” at Mcgill University: a History of Early Experimental Psychology in Postwar Canada
BUILDING A “CROSS-ROADS DISCIPLINE” AT MCGILL UNIVERSITY: A HISTORY OF EARLY EXPERIMENTAL PSYCHOLOGY IN POSTWAR CANADA ERIC OOSENBRUG A dissertation submitted to the Faculty of Graduate Studies in partial fulfillment of the requirements for the degree of Doctor of Philosophy Graduate Program in Psychology. Graduate Program in Psychology York University Toronto, Ontario October 2020 © Eric Oosenbrug, 2020 Abstract This dissertation presents an account of the development of psychology at McGill University from the late nineteenth century through to the early 1960s. The department of psychology at McGill represents an alternative to the traditional American-centered narrative of the cognitive revolution and later emergence of the neurosciences. In the years following World War II, a series of psychological experiments established McGill as among the foremost departments of psychology in North America. This thesis is an institutional history that reconstructs the origins, evolution, and dramatic rise of McGill as a major center for psychological research. The experiments conducted in the early 1950s, in the areas of sensory restriction, motivation, and pain psychology, were transformative in their scope and reach. Central to this story is Donald O. Hebb, author of The Organization of Behavior (1949), who arrived at McGill in 1947 to find the charred remains of a department. I argue that the kind of psychology Hebb established at McGill was different from most departments in North America; this is developed through a number of interwoven storylines focused on the understanding of a particular character of McGill psychology - a distinctive “psychological style” - and its broader historical importance for Canadian psychology, for North American psychology, and for psychology across the globe. -
Neuromodulated Dopamine Plastic Networks for Heterogeneous Transfer Learning with Hebbian Principle
S S symmetry Article Neuromodulated Dopamine Plastic Networks for Heterogeneous Transfer Learning with Hebbian Principle Arjun Magotra and Juntae Kim * Department of Computer Science and Engineering, Dongguk University, 30, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea; [email protected] * Correspondence: [email protected] Abstract: The plastic modifications in synaptic connectivity is primarily from changes triggered by neuromodulated dopamine signals. These activities are controlled by neuromodulation, which is itself under the control of the brain. The subjective brain’s self-modifying abilities play an essential role in learning and adaptation. The artificial neural networks with neuromodulated plasticity are used to implement transfer learning in the image classification domain. In particular, this has application in image detection, image segmentation, and transfer of learning parameters with significant results. This paper proposes a novel approach to enhance transfer learning accuracy in a heterogeneous source and target, using the neuromodulation of the Hebbian learning principle, called NDHTL (Neuromodulated Dopamine Hebbian Transfer Learning). Neuromodulation of plasticity offers a powerful new technique with applications in training neural networks implementing asymmetric backpropagation using Hebbian principles in transfer learning motivated CNNs (Convolutional neural networks). Biologically motivated concomitant learning, where connected brain cells activate positively, enhances the synaptic connection strength between the network neurons. Using the NDHTL algorithm, the percentage of change of the plasticity between the neurons of the CNN layer is directly managed by the dopamine signal’s value. The discriminative nature of transfer learning Citation: Magotra, A.; Kim, J. fits well with the technique. The learned model’s connection weights must adapt to unseen target Neuromodulated Dopamine Plastic datasets with the least cost and effort in transfer learning. -
Machine Awareness
Wright State University CORE Scholar International Symposium on Aviation International Symposium on Aviation Psychology - 2019 Psychology 5-7-2019 Machine Awareness Steven D. Harbour Jeffery D. Clark William D. Mitchell Krishnamurthy V. Vemuru Follow this and additional works at: https://corescholar.libraries.wright.edu/isap_2019 Part of the Other Psychiatry and Psychology Commons Repository Citation Harbour, S. D., Clark, J. D., Mitchell, W. D., & Vemuru, K. V. (2019). Machine Awareness. 20th International Symposium on Aviation Psychology, 480-485. https://corescholar.libraries.wright.edu/isap_2019/81 This Article is brought to you for free and open access by the International Symposium on Aviation Psychology at CORE Scholar. It has been accepted for inclusion in International Symposium on Aviation Psychology - 2019 by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. MACHINE AWARENESS Dr. Steven D. Harbour Dr. Jeffery D. Clark Mr. William D. Mitchell Riverside Research 2640 Hibiscus Way, Beavercreek, Ohio 45431, USA Dr. Krishnamurthy V. Vemuru Riverside Research 2900 Crystal Dr., Arlington, Virginia 22202, USA Current and future research that embodies a pathway to achieving machine common sense (MCS), including Capsule Neural Networks, Hebbian Plasticity Theory, Dual Process Theory, and machine awareness (MA). The final frontier may well involve a framework that is capable of machine curiosity, exploration, automatic self- direction and adaptation. The artificial intelligence (AI) system of the future will possess an innate curiosity and explore its own environment to gain knowledge, exhibiting a basic element of human cognition and awareness. The resulting MA system will possess inherent self-driven curiosity and related entropy in the decision space as it explores the environment in much the same manner as humans do.