DOCSLIB.ORG

Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications Eduardo Alonso City University London, UK Esther Mondragón Centre for Computational and Animal Learning Research, UK Medical inforMation science reference Hershey • New York Director of Editorial Content: Kristin Klinger Director of Book Publications: Julia Mosemann Acquisitions Editor: Lindsay Johnston Development Editor: Joel Gamon Publishing Assistant: Jamie Snavely Typesetter: Michael Brehm Production Editor: Jamie Snavely Cover Design: Lisa Tosheff Published in the United States of America by Medical Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2011 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or com- panies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Computational neuroscience for advancing artificial intelligence : models, methods and applications / Eduardo Alonso and Esther Mondragon, editors. p. cm. Summary: "This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro- psychological models and computational models"-- Provided by publisher. Includes bibliographical references and index. ISBN 978-1-60960-021-1 (hardcover) -- ISBN 978-1-60960-023-5 (ebook) 1. Computational neuroscience. 2. Neurosciences--Mathematical models. 3. Artificial intelligence. I. Alonso, Eduardo, 1967- II. Mondragon, Esther, 1965- QP357.5.C634 2011 612.80285'63--dc22 2010018588 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. 214 Chapter 10 Artificial Neural Systems for Robots Phil Husbands University of Sussex, UK Andy Philippides University of Sussex, UK Anil K. Seth University of Sussex, UK ABSTRACT This chapter reviews the use of neural systems in robotics, with particular emphasis on strongly biologi- cally inspired neural networks and methods. As well as describing work at the research frontiers, the paper provides some historical background in order to clarify the motivations and scope of work in this field. There are two major sections that make up the bulk of the chapter: one surveying the application of artificial neural systems to robot control, and one describing the use of robots as tools in neurosci- ence. The former concentrates on biologically derived neural architectures and methods used to drive robot behaviours, and the latter introduces a closely related area of research where robotic models are used as tools to study neural mechanisms underlying the generation of adaptive behaviour in animals and humans. INTRODUCTION isolated island. This often dark and dystopian play was a world-wide smash hit capturing the popular The idea of robots is rooted in the dreams and myths imagination as well as sparking much intellectual of old Europe and Ancient Greece in which me- debate (Horáková and Kelemen 2007). In the chanical men acted as either slaves or oppressors. process it helped forge the predominant image Indeed the modern word ‘robot’ was introduced of robots that now permeates our culture – that in Karel Čapek’s 1921 play R.U.R. (Rossum’s of life-like artificial creatures. Universal Robots) which told of artificial men In fact this image refers to what we now call manufactured as a source of cheap labour on an autonomous robots. In contrast to machines that perform precise repetitive tasks ad nauseam (e.g. robots used in manufacturing production lines), DOI: 10.4018/978-1-60960-021-1.ch010 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Artificial Neural Systems for Robots autonomous robots are required to behave in an (2002), Bekey (2005), Bekey and Goldberg(1993) appropriate way in a very broad range of circum- and Floreano and Mattiussi (2008). stances. Like biological creatures, their behaviour must be self-generated, making use of sensory information to moderate their responses to the HISTORY world. With these animal connotations, it is no surprise that when simple autonomous robots be- Despite the construction of many ingenious me- came a reality in the mid 20th century, researchers chanical automata over the centuries (including looked to natural nervous systems for inspiration chess playing Turks and flatulent ducks (Wood as they developed their early control systems. 2003)), it was not until the 1930s that devices The huge advances in our understanding of recognizable as robots (in the present day sense real neural networks over the past few decades, of the term) appeared. Early mobile robots, coupled with our development of increasingly such as Thomas Ross’s ‘robot rat’, completed sophisticated artificial varieties, has led to sig- in 1935, were designed for narrowly focused nificant growth in research on artificial neural single behaviours (often maze running) and em- systems in robotics. This chapter concentrates on ployed highly specific mechanisms to achieve the use of explicitly biologically inspired artificial their intended task (Cordeschi, 2002). These neural networks in autonomous robotics, review- ‘artificial animals’ inspired what were probably ing key applications and forms of neural system the very first examples of more general mobile used. There are a variety of drives underlying such autonomous robots – W. Grey Walter’s tortoises work, ranging from straightforward engineering (Walter, 1950). These robots were also the first to motivations – the desire to build better, smarter employ an early form of neural network as their machines – to purely scientific ones, particularly artificial nervous system. They were born out of in the use of robots as tools to study mechanisms the cybernetics movement, a highly interdisci- underlying the generation of adaptive behaviour plinary endeavour – drawing together pioneers in animals and humans. Often varying degrees of of computing and modern neuroscience – which both these motivations are present in any particular was the forerunner of much of contemporary AI project. This chapter will highlight work that falls and robotics, and the origin of artificial neural at the two extremes of this spectrum, as well as networks and evolutionary computing, as well much that rests in between. as control and information theory (Boden, 2006; The next section gives some historical back- Husbands et al., 2008). ground to the area in order to motivate the remain- In 1949, Walter, a neurologist and cyberneticist der of the chapter. Following that, there are two based at the Burden Institute in Bristol, UK, who major sections that make up the bulk of the paper: was also a world leader in EEG research, com- one on the application of artificial neural systems pleted a pair of revolutionary machines he called to robot control, and one on the use of robots as ‘tortoises’. The devices were three-wheeled and tools in neuroscience. The chapter closes with a sported a protective ‘shell’ (see Figure 1). They general discussion of prospects for such research. had a light sensor, touch sensor, propulsion motor, There is not enough space to give a comprehensive steering motor, and an electronic valve (vacuum review of all major work in the area, instead we tube) based analogue ‘nervous system’. Whereas have concentrated on a few important topics that earlier machines such as Ross’s were constrained to give a good overall flavour of research in the field. run on rails, the tortoises could roam freely around For more detailed coverage see e.g. Siciliano and their environment. Walter’s intention was to show Khatib (2008), Webb and Consi (2001), Ayers et al. that, contrary to the prevailing opinion at the time, 215 Artificial Neural Systems for Robots Figure 1. Grey Walter watches one of his tortoises even a very simple nervous system (the tortoises push aside some wooden blocks on its way back had two artificial neurons) could generate complex to its recharging hutch. Circa 1952 behaviour as long as there was a sufficiently large number of possible interaction patterns between the neurons (Walter, 1950). By studying whole embodied sensorimotor systems acting in the real world, he was pioneering a style of research that was to become very prominent in AI many years later, and remains so today (Brooks, 1999; Hol- land, 2003). Between Easter 1948 and Christmas 1949, he built the first tortoises, Elmer and Elsie. They were rather unreliable and required frequent attention. In 1951, his technician, Mr. W.J. ‘Bunny’ Warren, designed and built six new tortoises to a much higher standard. Three of these tortoises were exhibited at the Festival of Britain in 1951; others were regularly demonstrated in public throughout the 1950s. The robots were capable of phototaxis (steering
Load more

Recommended publications
  • Acceptability Study of A3-K3 Robotic Architecture for a Neurorobotics Painting
    ORIGINAL RESEARCH published: 10 January 2019 doi: 10.3389/fnbot.2018.00081 Acceptability Study of A3-K3 Robotic Architecture for a Neurorobotics Painting Salvatore Tramonte 1*, Rosario Sorbello 1*, Christopher Guger 2 and Antonio Chella 1,3 1 RoboticsLab, Department of Industrial and Digital Innovation (DIID), Universitá di Palermo, Palermo, Italy, 2 G.tec Medical Engineering GmbH, Schiedlberg, Austria, 3 ICAR-CNR, Palermo, Italy In this paper, authors present a novel architecture for controlling an industrial robot via Brain Computer Interface. The robot used is a Series 2000 KR 210-2. The robotic arm was fitted with DI drawing devices that clamp, hold and manipulate various artistic media like brushes, pencils, pens. User selected a high-level task, for instance a shape or movement, using a human machine interface and the translation in robot movement was entirely demanded to the Robot Control Architecture defining a plan to accomplish user’s task. The architecture was composed by a Human Machine Interface based on P300 Brain Computer Interface and a robotic architecture composed by a deliberative layer and a reactive layer to translate user’s high-level command in a stream of movement for robots joints. To create a real-case scenario, the architecture was Edited by: Ganesh R. Naik, presented at Ars Electronica Festival, where the A3-K3 architecture has been used for Western Sydney University, Australia painting. Visitors completed a survey to address 4 self-assessed different dimensions Reviewed by: related to human-robot interaction: the technology knowledge, the personal attitude, the Calogero Maria Oddo, innovativeness and the satisfaction. The obtained results have led to further exploring the Scuola Sant’Anna di Studi Avanzati, Italy border of human-robot interaction, highlighting the possibilities of human expression in Valery E.
    [Show full text]
  • Biomed Research International Special Issue on Brain Computer
    BioMed Research International Special Issue on Brain Computer Interface Systems for Neurorobotics: Methods and Applications CALL FOR PAPERS Brain computer interface (BCI) systems establish a direct communication between Lead Guest Editor the brain and an external device. ese systems can be used for entertainment, to Victor H. C. De Albuquerque, improve the quality of life of patients and to control Virtual Reality applications, Universidade de Fortaleza, Fortaleza, industrial machines, and robots. In the neuroscience eld such as in neuroreha- Brazil bilitation, BCIs are integrated into controlled virtual environments used for the [email protected] treatment of disability, for example, cerebral palsy, Down syndrome, and depression. Its aim is to promote a recovery of brain function lost due to a lesion through Guest Editors noninvasive brain stimulation (brain modulation) in a more accurate and faster Robertas Damaševičius, Kaunas manner than the traditional techniques. Neurorobotics combines BCIs with robotics University of Technology, Kaunas, aiming to develop articial limbs, which can act as real members of human body Lithuania being controlled from a brain-machine interface. With the advancement of a better [email protected] understanding of how our brain works, new realistic computational algorithms are being considered, making it possible to simulate and model specic brain functions Nuno M. Garcia, University of Beira for the development of new Computational Intelligence algorithms and, nally, BCI Interior, Covilhã, Portugal for mobile devices/apps. [email protected] As an augmentative communication channel, BCI has already attracted considerable Plácido R. Pinheiro, Universidade de research interest thanks to recent advances in neurosciences, wearable biosensors, Fortaleza, Fortaleza, Brazil and data mining.
    [Show full text]
  • 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.
    [Show full text]
  • Ethical and Social Aspects of Neurorobotics
    Science and Engineering Ethics https://doi.org/10.1007/s11948-020-00248-8 ORIGINAL RESEARCH/SCHOLARSHIP Ethical and Social Aspects of Neurorobotics Christine Aicardi2 · Simisola Akintoye3 · B. Tyr Fothergill1 · Manuel Guerrero4,5 · Gudrun Klinker6 · William Knight1 · Lars Klüver7 · Yannick Morel8 · Fabrice O. Morin6 · Bernd Carsten Stahl1 · Inga Ulnicane1 © The Author(s) 2020 Abstract The interdisciplinary feld of neurorobotics looks to neuroscience to overcome the limitations of modern robotics technology, to robotics to advance our understand- ing of the neural system’s inner workings, and to information technology to develop tools that support those complementary endeavours. The development of these tech- nologies is still at an early stage, which makes them an ideal candidate for proac- tive and anticipatory ethical refection. This article explains the current state of neu- rorobotics development within the Human Brain Project, originating from a close collaboration between the scientifc and technical experts who drive neurorobotics innovation, and the humanities and social sciences scholars who provide contextu- alising and refective capabilities. This article discusses some of the ethical issues which can reasonably be expected. On this basis, the article explores possible gaps identifed within this collaborative, ethical refection that calls for attention to ensure that the development of neurorobotics is ethically sound and socially acceptable and desirable. Keywords Neurorobotics · Ethics · Responsible Research and Innovation
    [Show full text]
  • Artificial Brain Project of Visual Motion
    In this issue: • Editorial: Feeding the senses • Supervised learning in spiking neural networks V o l u m e 3 N u m b e r 2 M a r c h 2 0 0 7 • Dealing with unexpected words • Embedded vision system for real-time applications • Can spike-based speech Brain-inspired auditory recognition systems outperform conventional approaches? processor and the • Book review: Analog VLSI circuits for the perception Artificial Brain project of visual motion The Korean Brain Neuroinformatics Re- search Program has two goals: to under- stand information processing mechanisms in biological brains and to develop intel- ligent machines with human-like functions based on these mechanisms. We are now developing an integrated hardware and software platform for brain-like intelligent systems called the Artificial Brain. It has two microphones, two cameras, and one speaker, looks like a human head, and has the functions of vision, audition, inference, and behavior (see Figure 1). The sensory modules receive audio and video signals from the environment, and perform source localization, signal enhancement, feature extraction, and user recognition in the forward ‘path’. In the backward path, top-down attention is per- formed, greatly improving the recognition performance of real-world noisy speech and occluded patterns. The fusion of audio and visual signals for lip-reading is also influenced by this path. The inference module has a recurrent architecture with internal states to imple- ment human-like emotion and self-esteem. Also, we would like the Artificial Brain to eventually have the abilities to perform user modeling and active learning, as well as to be able to ask the right questions both to the right people and to other Artificial Brains.
    [Show full text]
  • Identifiable Neuro Ethics Challenges to the Banking of Neuro Data
    ILLES J, LOMBERA S. IDENTIFIABLE NEURO ETHICS CHALLENGES TO THE BANKING OF NEURO DATA. MINN. J.L. SCI. & TECH. 2009;10(1):71-94. Identifiable Neuro Ethics Challenges to the Banking of Neuro Data Judy Illes ∗ & Sofia Lombera** Laboratory and clinical investigations about the brain and behavioral sciences, broadly defined as “neuroscience,” have advanced the understanding of how people think, move, feel, plan and more, both in good health and when suffering from a neurologic or psychiatric disease. Shared databases built on information obtained from neuroscience discoveries hold true promise for advancing the knowledge of brain function by leveraging new possibilities for combining complex and diverse data.1 Accompanying these opportunities are ethics challenges that, in other domains like the sharing of genetic information, have an impact on all parties involved in the research enterprise. The ethics and policy challenges include regulating the content of, access to, and use of databases; ensuring that data remains confidential and that informed consent © 2009 Judy Illes and Sofia Lombera. ∗ Judy Illes, Ph.D., Corresponding author, National Core for Neuroethics, The University of British Columbia. The authors are grateful to Dr. Jack Van Horn, Dr. Peter Reiner, Patricia Lau, and Daniel Buchman for valuable discussions on the future of neuro data banks. Parts of this paper were presented at the conference “Emerging Problems in Neurogenomics: Ethical, Legal & Policy Issues at the Intersection of Genomics & Neuroscience,” February 29, 2008, University of Minnesota, Minneapolis, Minnesota by Judy Illes. Full video of that conference is available at http://www.lifesci.consortium.umn.edu/conference/neuro.php. Supported by CIHR/INMHA CNE-85117, CFI, BCKDF, and NIH/NIMH # 9R01MH84282- 04A1.
    [Show full text]
  • New Technologies for Human Robot Interaction and Neuroprosthetics
    University of Plymouth PEARL https://pearl.plymouth.ac.uk Faculty of Science and Engineering School of Engineering, Computing and Mathematics 2017-07-01 Human-Robot Interaction and Neuroprosthetics: A review of new technologies Cangelosi, A http://hdl.handle.net/10026.1/9872 10.1109/MCE.2016.2614423 IEEE Consumer Electronics Magazine All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. CEMAG-OA-0004-Mar-2016.R3 1 New Technologies for Human Robot Interaction and Neuroprosthetics Angelo Cangelosi, Sara Invitto Abstract—New technologies in the field of neuroprosthetics and These developments in neuroprosthetics are closely linked to robotics are leading to the development of innovative commercial the recent significant investment and progress in research on products based on user-centered, functional processes of cognitive neural networks and deep learning approaches to robotics and neuroscience and perceptron studies. The aim of this review is to autonomous systems [2][3]. Specifically, one key area of analyze this innovative path through the description of some of the development has been that of cognitive robots for human-robot latest neuroprosthetics and human-robot interaction applications, in particular the Brain Computer Interface linked to haptic interaction and assistive robotics. This concerns the design of systems, interactive robotics and autonomous systems. These robot companions for the elderly, social robots for children with issues will be addressed by analyzing developmental robotics and disabilities such as Autism Spectrum Disorders, and robot examples of neurorobotics research.
    [Show full text]
  • On the Technology Prospects and Investment Opportunities for Scalable Neuroscience
    On the Technology Prospects and Investment Opportunities for Scalable Neuroscience Thomas Dean1,2,3 Biafra Ahanonu3 Mainak Chowdhury3 Anjali Datta3 Andre Esteva3 Daniel Eth3 Nobie Redmon3 Oleg Rumyantsev3 Ysis Tarter3 1 Google Research, 2 Brown University, 3 Stanford University Contents 1 Executive Summary 1 2 Introduction 4 3 Evolving Imaging Technologies 6 4 Macroscale Reporting Devices 10 5 Automating Systems Neuroscience 14 6 Synthetic Neurobiology 16 7 Nanotechnology 20 8 Acknowledgements 28 A Leveraging Sequencing for Recording — Biafra Ahanonu 28 B Scalable Analytics and Data Mining — Mainak Chowdhury 32 C Macroscale Imaging Technologies — Anjali Datta 35 D Nanoscale Recording and Wireless Readout — Andre Esteva 38 E Hybrid Biological and Nanotechnology Solutions — Daniel Eth 41 F Advances in Contrast Agents and Tissue Preparation — Nobie Redmon 44 G Microendoscopy and Optically Coupled Implants — Oleg Rumyantsev 46 H Opportunities for Automating Laboratory Procedures — Ysis Tarter 49 i 1 Executive Summary Two major initiatives to accelerate research in the brain sciences have focused attention on devel- oping a new generation of scientific instruments for neuroscience. These instruments will be used to record static (structural) and dynamic (behavioral) information at unprecedented spatial and temporal resolution and report out that information in a form suitable for computational analysis. We distinguish between recording — taking measurements of individual cells and the extracellu- lar matrix — and reporting — transcoding, packaging and transmitting the resulting information for subsequent analysis — as these represent very different challenges as we scale the relevant technologies to support simultaneously tracking the many neurons that comprise neural circuits of interest. We investigate a diverse set of technologies with the purpose of anticipating their devel- opment over the span of the next 10 years and categorizing their impact in terms of short-term [1-2 years], medium-term [2-5 years] and longer-term [5-10 years] deliverables.
    [Show full text]
  • Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: a Systematic Literature Review
    sensors Systematic Review Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review Nuraini Jamil 1 , Abdelkader Nasreddine Belkacem 2,* , Sofia Ouhbi 1 and Abderrahmane Lakas 2 1 Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] (N.J.); sofi[email protected] (S.O.) 2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] * Correspondence: [email protected] Abstract: Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive Citation: Jamil, N.; Belkacem, A.N.; BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used Ouhbi, S.; Lakas, A. Noninvasive for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from Electroencephalography Equipment the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited for Assistive, Adaptive, and to studies focusing on applications and devices of the BCI technology.
    [Show full text]
  • Subproject 10: Neurorobotics
    Co-funded by the European Union Subproject 10: Neurorobotics Deliverable D10.7.2 Revised Report, 6 Oct 2017 D10.7.2 (D60.2 D25, SGA1 M12) ACCEPTED 20171108.docx PU = Public 08-Nov-2017 Page 1 of 124 Co-funded by the European Union Project Number: 720270 Project Title: Human Brain Project SGA1 Document Title: D10.7.2 SP10 Neurorobotics Platform - Results for SGA1 Period 1 Document Filename: D10.7.2 (D60.2 D25, SGA1 M12) ACCEPTED 20171108.docx Deliverable Number: SGA1 D10.7.2 Deliverable Type: Report Work Package(s): All WPs in SP10 Dissemination Level: PU = Public Planned Delivery SGA1 M12 / 31 Mar 2017 Date: Submitted: 31 May 2017 (M14), Resubmitted 6 Oct 2017 (M19), Accepted 08 Nov Actual Delivery Date: 2017 Authors: Alois KNOLL, TUM (P56), Marc-Oliver Gewaltig, EPFL (P1) Compiling Editors: Florian RÖHRBEIN, TUM (P56) Contributors: All Task leaders and their representatives SciTechCoord Re- EPFL (P1): Jeff MULLER, Martin TELEFONT, Marie-Elisabeth COLIN view: UHEI (P47): Martina SCHMALHOLZ, Sabine SCHNEIDER Editorial Review: EPFL (P1): Guy WILLIS, Colin McKINNON Abstract: This document describes the progress made in the first half of the first SGA. Keywords: Neurorobotics, virtual robotics, in silico experiments D10.7.2 (D60.2 D25, SGA1 M12) ACCEPTED 20171108.docx PU = Public 08-Nov-2017 Page 2 of 124 Co-funded by the European Union Table of Contents 1. Preamble ........................................................................................................ 8 2. SP Leader’s Overview........................................................................................
    [Show full text]
  • Editorial Brain Computer Interface Systems for Neurorobotics: Methods and Applications
    Hindawi BioMed Research International Volume 2017, Article ID 2505493, 2 pages https://doi.org/10.1155/2017/2505493 Editorial Brain Computer Interface Systems for Neurorobotics: Methods and Applications Victor Hugo C. de Albuquerque,1 Robertas DamaševiIius,2 Nuno M. Garcia,3 Plácido Rogério Pinheiro,1 and Pedro P. Rebouças Filho4 1 Programa de Pos´ Graduac¸ao˜ em Informatica´ Aplicada, Laboratorio´ de Bioinformatica,´ Universidade de Fortaleza, Fortaleza, CE, Brazil 2Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania 3Department of Informatics, Instituto de Telecomunicac¸oes˜ and ALLab Assisted Living Computing and Telecommunications Laboratory,UniversidadedaBeiraInterior,Covilha,˜ Portugal 4Laboratorio´ de Processamento de Imagens e Simulac¸ao˜ Computacional, Instituto Federal de Educac¸ao,˜ Cienciaˆ e Tecnologia do Ceara,´ Maracanau,´ CE, Brazil Correspondence should be addressed to Victor Hugo C. de Albuquerque; [email protected] Received 7 August 2017; Accepted 7 August 2017; Published 30 October 2017 Copyright © 2017 Victor Hugo C. de Albuquerque et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Brain computer interface (BCI) systems establish a direct data mining. However, to overcome numerous challenges the communication between the brain and an external device. BCI technology still requires research in high-performance These systems can be used for entertainment, to improve the and robust signal processing and machine learning algo- quality of life of patients and to control virtual and augmented rithmstoproduceareliableandstablecontrolsignalfrom reality applications, industrial machines, and robots. In the nonstationary brain signals to allow development of real- neuroscience field such as in neurorehabilitation, BCIs are life BCI systems usable across many individuals.
    [Show full text]
  • Human-Inspired Eigenmovement Concept Provides Coupling-Free Sensorimotor Control in Humanoid Robot
    ORIGINAL RESEARCH published: 25 April 2017 doi: 10.3389/fnbot.2017.00022 Human-Inspired Eigenmovement Concept Provides Coupling-Free Sensorimotor Control in Humanoid Robot Alexei V. Alexandrov 1, Vittorio Lippi 2, Thomas Mergner 2*, Alexander A. Frolov 1, 3, Georg Hettich 2 and Dusan Husek 4 1 Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia, 2 Department of Neurology, University Clinics of Freiburg, Freiburg, Germany, 3 Russian National Research Medical University, Moscow, Russia, 4 Institute of Computer Science, Academy of Science of the Czech Republic, Prague, Czechia Control of a multi-body system in both robots and humans may face the problem of destabilizing dynamic coupling effects arising between linked body segments. The state of the art solutions in robotics are full state feedback controllers. For human hip-ankle coordination, a more parsimonious and theoretically stable alternative to the robotics solution has been suggested in terms of the Eigenmovement (EM) control. Eigenmovements are kinematic synergies designed to describe the multi DoF system, and its control, with a set of independent, and hence coupling-free, scalar equations. This paper investigates whether the EM alternative shows “real-world robustness” against noisy and inaccurate sensors, mechanical non-linearities such as dead zones, and human-like feedback time delays when controlling hip-ankle movements of a balancing humanoid robot. The EM concept and the EM controller are introduced, the robot’s Edited by: dynamics are identified using a biomechanical approach, and robot tests are performed in Massimo Sartori, University of Göttingen, Germany a human posture control laboratory. The tests show that the EM controller provides stable Reviewed by: control of the robot with proactive (“voluntary”) movements and reactive balancing of Yury Ivanenko, stance during support surface tilts and translations.
    [Show full text]