ECMS 2013 Conference Proceedings

Copyright © ECMS2013 Printed: ISBN: 978-0-9564944-6-7 European Council for Modelling

CD: ISBN: 978-0-9564944-7-4 and Simulation

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Competence Cente r

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PROCEEDINGS

27 ^th European Conference on Modelling and Simulation ECMS 2013

May 27

– May 30

, 2013 lesund, Norway

Edited by:

Webjørn Rekdalsbakken Robin T. Bye

Houxiang Zhang

Organized by:

ECMS - European Council for Modelling and Simulation

Hosted by:

Aalesund University College, Norway

Sponsored by:

Aalesund University College, Norway The Research Council of Norway

Møre and Romsdal County Municipality Rolls-Royce Marine

Offshore Simulator Centre AS Farstad Shipping

Sparebanken Møre Tekna

Norwegian Maritime Competence Cente r

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International Co-Societies:

IEEE - Institute of Electrical and Electronics Engineers ASIM - German Speaking Simulation Society

EUROSIM - Federation of European Simulation Societies PTSK - Polish Society of Computer Simulation

LSS - Latvian Simulation Society

V

ECMS 2013 ORGANIZATION

Conference Chair Webjørn Rekdalsbakken

Aalesund University College Norway

Conference Co-Chair Robin T. Bye

Aalesund University College Norway

Programme Chair Robin T. Bye

Aalesund University College Norway

Programme Co-Chair Houxiang Zhang

Aalesund University College Norway

President of European Council for Modelling and Simulation Evtim Peytchev

Nottingham Trent University United Kingdom

Managing Editor Martina-Maria Seidel

St. Ingbert

Germany

VI

INTERNATIONAL PROGRAMME COMMITTEE

Agent-Based Simulation

Track Chair: Michael Möhring

University of Koblenz-Landau, Germany Co-Chair: Ulf Lotzmann

University of Koblenz-Landau, Germany

Simulation in Industry, Business and Services Track Chair: Alessandra Orsoni

University of Kingston, United Kingdom Co-Chair: Serhiy Kovela

University of Kingston, United Kingdom Co-Chair: Arne Petermann

Berlin University for Professional Studies, Germany

Simulation of Intelligent Systems

Track Chair: Zuzana Kominková Oplatková Tomas Bata University of Zlín, Czech Republic Co-Chair: Roman Senkerik

Tomas Bata University of Zlín, Czech Republic

Finance, Economics and Social Science Track Chair: Javier Otamendi

University of Rey Juan Carlos Madrid, Spain Co-Chair: Barbara Dömötör

Corvinius University of Budapest, Hungary

Simulation of Complex Systems & Methodologies Track Chair: Krzysztof Amborski

Warsaw University of Technology, Poland Co-Chair: Jaroslav Sklenar

University of Malta, Malta

VII

Simulation, Experimental Science and Engineering in Maritime Operations Track Chair: Hans Petter Hildre

Aalesund University College, Norway

Co-Chair: Sashidharan Komandur Aalesund University College, Norway

Simulation and Visualization for Training and Education Track Chair: Vilmar Æsøy

Aalesund University College, Norway

Co-Chair: Eilif Pedersen

Norwegian University of Science and Technology, Norway

Modelling, Simulation and Control of Technological Processes Track Chair:

Tomas Bata University in Zlín, Czech Republic Co-Chair: Petr Dostál

Tomas Bata University in Zlín, Czech Republic Co-Chair:

Tomas Bata University in Zlín, Czech Republic

Discrete Event Modelling and Simulation in Logistics, Transport and Supply Chain Management

Track Chair: Gaby Neumann

Technical University of Applied Sciences Wildau, Germany Co-Chair: Edward J. Williams

University of Michigan-Dearborn, USA

High Performance Modelling and Simulation Track Chair: Joanna Kolodziej

Institute of Computer Science Cracow University of Technology, Poland Co-Chair: Horacio-Gonzalez-Velez

National College of Ireland Dublin, Ireland

Co-Chair: Ewa Niewiadomska-Szynkiewicz

NASK, Warsaw, Poland

VIII Policy Modelling

Track Chair: Maria Wimmer

University of Koblenz-Landau, Germany Programme Chair: Scott Moss

University of Koblenz-Landau, Germany Visiting Professor

Modelling and Simulation in Computer Vision for Image Understanding Track Chair: Stephen Chen

Zhejiang University of Technology, China Co-Chair: Mai Xu

Tsinghua University, China

Simulation and Optimization

Track Chair: Frank Herrmann

University of Applied Sciences Regensburg, Germany Co-Chair: Erik Krobat

University of Bundeswehr in Munich, Germany Co-Chair: Thorsten Claus

International Graduate School (IHI) Zittau, Germany

Modeling and Simulation in Robotic Applications Track Chair: Wei Wang

Beijing University of Aeronautics and Astronautics, China Co-Chair: Sigal Berman

Ben-Gurion University of the Negev, Israel

Simulation and Computational Neuroscience Track Chair: Yasser Roudi

Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology, Norway Co-Chair: Gaute Einevoll

Norwegian University of Life Science (UMB), Norway

Simulation of Social Interaction Track Chair: Bruce Edmonds

Manchester Metropolitan University Business School, United Kingdom Co-Chair: Flaminio Squazzoni

University of Brescia, Italy

IX

IPC Members in Alphabetical Order

Pavel O. Abaev, Peoples' Friendship University of Russia, Russia Petra Ahrweiler, University College Dublin, Ireland

Athena Akrami, SISSA, Italy

Marco Aldinucci, University of Turin, Italy

Frédéric Amblard, University of Toulouse, France

Piotr Arabas, Warsaw University of Technology, NASK, Poland

Hans-Peter Barbey, University of Applied Sciences in Bielefeld, Germany Shusheng Bi, Robotics Institute BeiHang University in Beijing, China Kashif Bilal, North Dakota State University, USA

Ove Bjørneset, STX-OSV, Norway

Frøy Birthe Bjørneset, Rolls-Royce Marine, Norway Giangiacomo Bravo, University of Turin, Italy

Øyvind Bunes, Rolls-Royce Marine, Norway

Aleksander Byrski, AGH Univ. of Science and Technology, Poland Piers Campbell, The University of Newcastle, Callaghan, Australia Steve Capes, Cambridge Shire County Council, United Kingdom Ester Camiña Centeno, University Complutense Madrid, Spain

Krzysztof Cetnarowicz, AGH Univ. of Science and Technology, Poland Petr Chalupa, Tomas Bata University in Zlín, Czech Republic

Edmund Chattoe-Brown

University of Leicester, United Kingdom Dan Chen, China University of Geosciences, Wuhan, China

Catherine Cleophas, FU-Berlin, Germany

Péter Csóka, Corvinus University of Budapest, Hungary Mate J. Csorba, Marine Cybernetics, Norway

Peter De Smedt, Research Center of the Flemish Government, Belgium Ciprian Dobre, Politechnical University of Bucharest, Romania

Sanja Dogramadzi, University of the West of England, United Kingdom Luis Miguel Doncel Pedrera, University Rey Juan Carlos Madrid, Spain Bernabé Dorronsoro, University of Lille, France

ek Duek, University of Pardubice, Czech Republic Andrzej Dzielinski, Warsaw University of Technology, Poland Yael Edan, Ben-Gurion University, Israel

Atis Elsts, EDI, Latvia

X

Cain Evans, Birmingham City University, United Kingdom Doron Friedman, Interdisciplinary Center, Israel

Pilar Grau-Carles, University Rey Juan Carlos Madrid, Spain Dag. Sverre Gronmyr, Rolls Royce Marine, Norway

Antoni Guasch, UPC, Barcelona Tech, Spain

Weidong Guo, Robotics Institute BeiHang University in Beijing, China David Hales, Open University, United Kingdom

Dániel Havran, Corvinius University of Budapest, Hungary

Poul Heegaard, Norwegian Univ. of Science and Technology, Trondheim, Norway Thomas Hellström, Umea University, Sweden

Karl Henning Halse, Aalesund University College, Norway Gerald Holowicki, University of Michigan-Dearborn, USA Daniel Honc, University of Pardubice, Czech Republic

Mark Hoogendorn, VU University of Amsterdam, The Netherlands Thomas Hußlein, OptWare GmbH in Regensburg, Germany Martin Ihrig, University of Pennsylvania, United States Teruaki Ito, University of Tokushima, Japan

Yumi Iwashita, Kyushu University, Japan Luis Izquierdo, University of Burgos, Spain

Wander Jager, University of Groningen, The Netherlands Mo Jamshidi, The University of Texas at San Antonio, USA Marijn Janssen, TU Delft, The Netherlands

Marco Janssen, Arizona State University, United States Cara H. Kahl, TU Hamburg-Harburg, Germany

Osman Khalid, North Dakota State University, Fargo, USA Zaheer Abbas Khan, CERN, Switzerland

Natalie Kliewer, FU Berlin, Germany

Frank Klingert, TU Hamburg-Harburg, Germany

Petia Koprinkova-Hristova, Bulgarian Academy of Sciences, Bulgaria Victor Y. Korolev, Lomonosov Moscow State University, Russia Igor Kotenko, St. Petersburg Institute, Russia

Martina Kotyrba

University of Ostrava, Czech Republic

Marek Kubalcik

Tomas Bata University in Zlín, Czech Republic

Jane Labadin, University Malaysia Sarawak, Malaysia

XI

Jiting Li, Robotics Institute BeiHang University in Beijing, China Dario G. Liebermann, Tel-Aviv University, Israel

Henrik Lindén, KTH, Sweden

Sheng Liu, Zhejiang University of Technology, China

Rong Liu, Robotics Institute BeiHang University in Beijing, China Arne Løkketangen, Molde University College, Norway

Ahmad Lotfi, Nottingham Trent University, United Kingdom Euripidis Loukis, Aegean-Research Unit, Greece

David Ludlow, University of West England, United Kingdom Susan Lysecky, University of Arizona, United States

Przemyslaw Majewski, Fido Intelligence, Poland

Saif U. R. Malik, North Dakota State University, Fargo, USA Michael Manitz, University Duisburg-Essen, Germany Michal Marks, NASK Warsaw, Poland

, VUCHT, Slovakia

, Tomas Bata University in Zlín, Czech Republic Artis Mednis, EDI, Latvia

Weiliang Meng, NLPR, China

Nicolas Meseth, University of Osnabruck, Germany Hermann Meuth, University of Applied Science, Germany Michela Milano, University of Bologna, Italy

Yuri Misnikov, University of Leeds, United Kingdom Christian Müller, TH Wildau, Germany

Nazmun Nahar, University of Jyvaskyla, Finland

Pavel Nahodil, Czech Technical University of Prague, Czech Republic Libero Nigro, University of Calabria, Italy

Yifeng Niu, University of Hamburg, Germany

Jakub Novák, Tomas Bata University in Zlín, Czech Republic Felix Obschonka, FU Berlin, Germany

Adegboyega Ojo, DERI, Ireland

Dominik Olszewski, Warsaw University of Technology, Poland Johan Oppen, Molde University College, Norway

Paul Ormerod, Volterra, United Kingdom

Ottar L. Osen, Aalesund University College, Norway

XII

Nazmiye Ozkan, Univ. of Westminster, United Kingdom Mario Paolucci, ISTC/CNR, Italy

!"#, Poznan University of Technology, Poland Johnatan E. Pecero, University of Luxembourg, Luxembourg

Alexander V. Pechinkin, Russian Academy of Sciences and Peoples', Russia Anna Plichta, Cracow University of Technology, Poland

Gary Polhill, James Hutton Institute Aberdeen Scotland, United Kingdom Dmitriy Ponkratov, Aalesund University College, Norway

Matthijs Pontier, VU University Amsterdam, The Netherlands Florian Pop, Polithechnical University of Bucharest, Romania Ioan Popa, University of Craiova, Romania

Roberto Protil, Federal University of Viçosa, Brazil

Rostislav V. Razumchik, Russian Academy of Sciences and Peoples', Russia Karl-Johan Reite, SINTEF Fisheries & Agriculture, Norway

Napoleon H. Reyes, Massey University, New Zealand Young Ro, University of Michigan-Dearborn, United States Boris Rohal-Ilkiv, Technical University of Bratislava, Slovakia Juliette Rouchier, GREQAM/CNRS, France

Toni Ruohonen, University of Jyväskylä, Finland

Konstantin E. Samoylov, Peoples' Friendship University of Russia, Russia Hans Georg Schaathun, Aalesund University College, Norway

Sabrina Scherer, University of Koblenz-Landau, Germany

Thomas Schulze

Otto-von-Guericke University Magdeburg, Germany Weiguo Sheng, Zhejiang University of Technology, China

Zvi Shiller, Ariel University Center, Israel

Peer-Olaf Siebers, University of Nottingham, United Kingdom

Anders Skoogh, Chalmers University of Technology, Goteborg, Sweden Andrzej Sluzek, Technical University of Singapore, Singapore

Katarzyna Smelcerz, Cracow University of Technology, Poland Roman Smierzchalski, Gdansk University of Technology, Poland Trygve Solstad, NTNU, Norway

Mojca Indihar Stemberg, University of Ljubljana, Slovenia

Girts Strazdins, Aalesund University College, Norway

Yipeng Sun, Tsinghua University, China

XIII

Magdalena Szmajduch, CDN-Partner Cracow, Poland Károly Takács, Corvinius University of Budapest, Hungary Themis Tambouris, University of Macedonia, Greece Tom Tetzlaff, FZ Juelich, Germany

Peter Trkman, University of Ljubljana, Slovakia

Klaus G. Troitzsch, University Koblenz-Landau, Germany

Christopher Tubb, University of Wales Newport, United Kingdom Kata Váradi, Corvinus University of Budapest, Hungary

Harko Verhagen, Stockholm University, Sweden

Alexey A. Voinov, University of Twente, The Netherlands Eva Volna, University of Ostrava, Czech Republic

Dangxiao Wang, Robotics Institute BeiHang University in Beijing, China Roland Wertz, Fraunhofer IPA Stuttgart, Germany

Tomasz Wiktor Wlodarczyk, University of Stavanger, Norway Aree Witoelar, NTNU, Norway

Daniel Wójcik, Nencki Institute, Poland Weili Xiong, Jiangnan University, China

Pan Yushan, Aalesund University College, Norway Michael Zaggl, TU Hamburg-Harburg, Germany Jianhua Zhang, University of Hamburg, Germany

Yuru Zhang, Robotics Institute BeiHang University in Beijing, China

Marcello Zottolo, Lee Memorial Health System, United States

XIV

XV

PREFACE

We at the Aalesund University College (AAUC) are grateful for having the honour of hosting the 27th European Conference on Modelling and Simulation, or ECMS 2013.

Located in the Art Nouveau town of Ålesund, AAUC lies at the heart of the maritime industry cluster on the west coast of Norway. With five faculties and more than 2000 students and 200 staff, the university college offers a wide range of study programmes in engineering, maritime operations, biotechnology, international business, and health science, including a newly accredited master programme in simulation and visualisation. Rooted in our strategy plan is a strong focus on applied maritime technology and operations, which manifests itself through some of the world's most advanced offshore simulators and our close ties to the maritime industry in both training, education, and research. As a result, modelling and simulation permeate almost everything we do at AAUC, from basic engineering courses, through training of offshore personnel, to novel research.

Across all tracks, this year's conference has received excellent contributions from some of the world's most highly regarded researchers in their fields. In particular, with the human brain as a common denominator, keynote speakers Stephen Grossberg and May-Britt Moser present their respective work on grid cells, place cells, and brain maps for space; Peter and Megan Neilson introduce their adaptive model theory and human movement control systems; and Sigal Berman talks about telerobotics and hand gestures for remote control. Having secured these keynote speakers has also spawned the creation of new tracks in robotic applications and computational neuroscience, the latter of which include specially invited talks by Peter Latham and Matteo Marsili.

We are very proud that six brand new tracks are introduced at ECMS 2013, dealing with modelling, simulation and visualisation in training and education; maritime technology and operations; robotic applications; computational neuroscience;

optimization; computer vision; and social interaction. In addition, ten traditional tracks are offered in topics such as agent-based simulation; complex systems; finance, economics and social science; high performance modelling and simulation; industry, business, and services; intelligent systems; logistics, transport, and management;

process control; and policy modelling. In total, some 141 accepted papers and oral talks are presented, with more than 170 participants from all over the world attending the sessions.

For 27 years, ECMS has set the stage for an independent, highly interdisciplinary scientific forum for researchers and practitioners from all over the world to present and discuss the latest findings, challenges, and future directions of their work. We, the organisers of ECMS 2013, hope that the high quality of accepted papers and participants as well as our exciting scientific, social, and cultural programme all ensure the success of this year's conference and we look forward in excitement to which directions the conference will take in the future.

Webjørn Rekdalsbakken Robin T. Bye Houxiang Zhang

Conference Chair Conference Co-Chair Programme Co-Chair

Programme Chair

XVI

XVII

TABLE OF CONTENTS

Plenary Talks

Coordinated Learning Of Grid Cell And Place Cell Spatial And Temporal Properties: Multiple Scales, Attention, And Oscillations - Extended Abstract

Stephen Grossberg ... 5

Brain Maps For Space - Abstract

May-Britt Moser ... 9

Adaptive Model Theory: A History - Extended Abstract

Megan D. Neilson, Peter D. Neilson ... 10

Adaptive Model Theory: Modelling The Modeller - Extended Abstract

Peter D. Neilson, Megan D. Neilson ... 12

Hand Gestures For Remote Control - Extended Abstract

Sigal Berman ... 15

Invited Talks for Computational Neuroscience

On Modeling And Sampling Complex Systems - Abstract

Matteo Marsili ... 21

Olfaction As Probabilistic Inference - Abstract

Peter Latham ... 22

Agent-Based Simulation

Personality Simulation In Interactive Agents Through Emotional Biasis

José Serra, Pedro Nogueira ... 25

Simulation Of Incentive Mechanisms For Renewable Energy Policies

Andrea Borghesi, Michela Milano, Marco Gavanelli, Tony Woods ... 32

Effect Of Declaration On Emergence Of Cooperation In Demographic Donor-Recipient Game

Tsuneyuki Namekata

Yoko Namekata ... 39

XVIII

An Agent-Based Model To Simulate Pathogen Transmission Between Aquaculture Sites In The Romsdalsfjord

Saleh Alaliyat, Ottar L. Osen, Kristina Øie Kvile ... 46

Behavioural Queuing With Interacting Customers And Service Providers:

A Simulation Based Approach

Carlos Arturo Delgado-Alvarez, Ann van Ackere, Erik R. Larsen ... 53

Experiments With Simulation Of Botnets And Defense Agent Teams

Igor Kotenko ... 61

Agent Methodological Layers In Repast Simphony

Franco Cicirelli, Angelo Furfaro, Libero Nigro, Francesco Pupo ... 68

Interactive, GPU-Based Urban Growth Simulation For Agile Urban Policy Modelling

Michel Krämer, Andreas Kehlenbach... 75

Simulation of Complex Systems and Methodologies

Pseudo-Code Simulation Of Designer Activity In Conceptual Designing Of Software Intensive Systems

Petr Sosnin ... 85

Modeling And Simulation Semantics For Building Large-Scale Multi-Domain Embedded Systems

Joshua D. Carl, Zsolt Lattman, Gautam Biswas ... 93

The Discrete Event Simulation Framework DESMO-J: Review, Comparison To Other Frameworks And Latest Development

Johannes Göbel, Philip Joschko, Arne Koors, Bernd Page ... 100

Tool For Discrete Event Simulation In Matlab

Jaroslav Sklenar ... 110

Modelling Of Mist Reactor: Effect Of Packing Fraction And Film Thickness On The Growth Of Hairy Roots

Manish Vashishtha, Kumar Saurabh... 117

Simulating Public Private Networks As Evolving Systems

Ameneh Deljoo, Marijn Janssen, Bram Klievink ... 124

Bifurcation Effects In Degenerate Differential Models Of Subpopulation Dynamics

Serge V. Chernyshenko, Olexandr O. Kuzenkov ... 130

XIX

Methods Used To Develop Hydrogeological Model Of Latvia Aivars Spalvins, Janis Slangens, Inta Lace, Kaspars Krauklis,

Olgerts Aleksans ... 136

Structure Adaptation Of Models Describing Scheduling Processes In Complex Technical-Organizational System (CTOS)

Dmitry Ivanov, Boris V. Sokolov, Semyon A. Potryasaev,

Vjasheslav A. Zelentsov, Olga V. Brovkina ... 143

Robust Control Of Air-Flow In Air-Heating Set

Marek Dlapa ... 149

Simulation, Experimental Science and Engineering in Maritime Operations

Using An HLA Simulation Environment For Safety Concept Verification Of Offshore Operations

Christoph Läsche, Volker Gollücke, Axel Hahn ... 156

Virtual Obeya: Collaborative Tools And Approaches To Boost The Use Of Simulators In Concept Design

Detlef Blankenburg, Kjetil Kristensen, Knut Einar Aasland,

Ole Ivar Sivertsen ... 163

Emerging Tools For Conceptual Design: The Use Of Game Engines To Design Future User Scenarios In The Fuzzy Front End Of Maritime Innovation

Snorre Hjeslath ... 170

Tactile Cues For Ship Bridge Operations

Yushan Pan, Sathiya Kumar Renganayagalu, Sashidharan Komandur ... 177

Hierarchical Task Analysis, Situation-Awareness And Support Software Hans Georg Schaathun, Magne Aarset, Runar Ostnes,

Robert Rylander ... 184

Propulsion Machinery Operating In Ice – A Modelling And Simulation Approach

... 191

Control Design For Slow Speed Positioning

Anna Witkowska ... 198

Kinect-Based Systems For Maritime Operation Simulators?

Girts Strazdins, Sashidharan Komandur, Arne Styve ... 205

Towards A Design Simulator For Offshore Ship Bridges

Helge T. Kristiansen, Kjetil Nordby ... 212

XX

Simulation and Visualization for Training and Education

Preliminary Experiments With EVA - Serious Games Virtual Fire Drill Simulator José Fernando M. Silva, João Emílio Almeida, António Pereira,

Rosaldo J. F. Rosetti, António Leça Coelho ... 221

A Design Space Exploration Framework For Automotive Embedded Systems And Their Power Management

Gregor Walla, Zaur Molotnikov, Hans-Ulrich Michel, Walter Stechele,

Andreas Barthels, Andreas Herkersdorf ... 228

Flexible Modeling And Simulation Architecture For Haptic Control Of Maritime Cranes And Robotic Arm

Filippo Sanfilippo, Hans Petter Hildr

Eilif Pedersen ... 235

Methodology Of Tolerance Synthesis Using Bond Graph Van Hoa Ngyyen, Damien Eberard, Wilfrid Marquis-Favre,

Laurent Krahenbuhl ... 243

A Novel Approach To Anti-Sway Control For Marine Shipboard Cranes Siebe B. van Albada, G. Dick van Albada, Hans Petter Hildre,

Houxiang Zhang ... 249

Finance, Economics and Social Science

Gender Differences In Capacity Auctions:

A Simulation Experiment With econport

F. Javier Otamendi, Luis Miguel Doncel ... 259

Towards JAVA Simulation Experiment With Agent-Based Trading Processes

!"#$$%&'$#()$ ... 264

Assessing The Severity Of Recreational Boating Accidents

*+/01&/2!34)4... 269

Cost Simulation Of An Inflation-Linked And A Floater Bond With Backtesting

5&)607-Dancs ... 275

XXI

Modelling Optimal Hedge Ratio In The Presence Of Funding Risk

Barbara Dömötör ... 282

The Modified Empirical Mode Decomposition Method For Analysing The Cyclical Behavior Of Time Series

Vladimir Sebesta, Roman Marsalek, Jitka Pomenkova... 288

Simulation in Industry, Business and Services

A Smartphone Application For The Monitoring Of Domestic Consumption Of Electricity

Franco Cicirelli, Emmanuele Neri, Libero Nigro, Francesco Pupo ... 295

Investigating The Use Of Semantic Technologies In Spatial Mapping Applications

Taha Osman, Luke Shires, Tope Omitola, Nigel Shadbolt,

Jeremy Hague ... 301

A Study Of Cost Effective Scheduling Of Nurses Based On The Domain Transformation Method

Geetha Baskaran, Andrzej Bargiela, Rong Qu ... 309

A Tutorial On Modelling Call Centres Using Discrete Event Simulation

Benny Mathew, Manoj K. Nambiar... 315

The Influence Of Management For Breaking Organizational Paths - A Simulation Study

Felix Obschonka, Arne Petermann ... 322

Agent-Based Simulation As A Support For Price-Setting In Passenger Transport

Norman Kellermann ... 333

Agent-Based Simulation Analysis Of Path Dependence In Corporate IS Networks For Strategic IT Management

Daniel Fürstenau ... 340

XXII

Simulation of Intelligent Systems

Methodology For Elliott Waves Pattern Recognition

'&5&80)'7/($abiballa,

David Brazina ... 349

Iris Data Classification By Means Of Pseudo Neural Networks Based On Evolutionary Symbolic Regression

Zuzana Kominkova Oplatkova, Roman Senkerik ... 355

Modelling And Reasoning With Fuzzy Logic Redundant Knowledge Bases Hashim Hab80)'7/($'&5&8 ... 361 Predator-Prey Simulation’s Parameters And Leverage Points

'7/($)70570) Martin Kotyrba,

Hashim Habiballa ... 367

Collaborative Data Dissemination Methods In VANETs For Identifying Road Conditions Zone Boundaries

Emadeddin A. Gamati, Richard Germon, Evtim Peytchev ... 372

Autonomous Design Of Modular Intelligent Systems

09/0:&$; ... 379

Scheduling The Flow Shop With Blocking Problem With The Chaos-Induced Discrete Self Organising Migrating Algorithm

Donald Davendra, Magdalena Bialic-Davendra, Roman Senkerik,

Michal Pluhacek ... 386

Multiple Choice Strategy For PSO Algorithm – Performance Analysis On Shifted Test Functions

Michal Pluhacek, Roman Senkerik, Ivan Zelinka, Donald Davendra ... 393

Analytic Programming In The Task Of Evolutionary Synthesis Of The Robust Controller For Selected Discrete Chaotic Systems

Roman Senkerik, Zuzana Kominkova Oplatkova, Ivan Zelinka,

Michal Pluhacek ... 398

XXIII

Modelling, Simulation and Control of Technological Processes

Design And Simulation Of Self-Tuning Predictive Control Of Time-Delay Processes

:<8)'$58=$, &&) ... 407

Modeling Of Alcohol Fermentation In Brewing – Comparative Assessment Of Flavor Profile Of Beers Produced With Free And Immobilized Cells

Stoyan Vassilev, Vessela Naydenova, Mariana Badova, Vasil Iliev,

Maria Kaneva, Georgi Kostov, Silviya Popova ... 415

Database Of Unstable Systems:

A New Site For Models Of Unstable Processes

*&($34(/0 5>:$ ... 422

Hybrid Adaptive LQ Control Of Chemical Reactor

/?&$ &&) ... 428 Predictive Versus Vector Control Of The Induction Motor

Sergiu Ivanov, Virginia Ivanov, Vladimir Rasvan, Eugen Bobasu,

Dan Popescu, Florin Stinga ... 434

State-Space Constrained Model Predictive Control

7*&($($ ... 441

Saturation Relay vs. Relay Transient Identification Tests For A TDS Model

@8 $>! $# ... 446 Simulation Model Of The Municipal Heat Distribution Systems

Lubomir Vasek, Viliam Dolinay ... 453

Mathematical Modeling Of Bacterial Cellulose Production By Acetobacter Xylinum Using Rotating Biological Fermentor

D.M.S.C. Dissanayake, F. M. Ismail ... 459

XXIV

High Performance Modelling and Simulation

Lightweight Distributed Component-Oriented Multi-Agent Simulation Platform

Daniel Krzywicki, Lukasz Faber, Kamil Pietak, Aleksander Byrski,

Marek Kisiel-Dorohinicki ... 469 Real Life Data Acquisition In Wireless Sensor Network Localization System

Michal Marks ... 477 Simulation Of Energy-Aware Backbone Networks

Ewa Niewiadomska-Szynkiewicz, Andrzej Sikora, Marcin Mincer,

Piotr Arabas ... 483 Bio-Inspired Rate Control For Multi-Priority Data Transmission Over WMSN

Xin-Wei Yao, Wan-Liang Wang, Shuang-Hua Yang ... 490 A Toolchain For Profiling Virtual Machines

Jiaqi Zhao, Jie Tao, Lizhe Wang, Andreas Wirooks ... 497 Genetic-Based Solutions For Independent Batch Scheduling In Data Grids

Joanna Kolodziej, Magdalena Szmajduch, Lizhe Wang, Dan Chen ... 504 A Performance Modeling Language For Big Data Architectures

Enrico Barbierato, Marco Gribaudo, Mauro Iacono ... 511 Towards The Deployment Of Fastflow On Distributed Virtual Architectures

Sonia Campa, Marco Danelutto, Massimo Torquati,

Horacio González-Vélez, Alina Mădălina Popescu ... 518 Efficiency Of Memetic And Evolutionary Computing In Combinatorial

Optimisation

Magdalena Kolybacz, Michal Kowol, Lukasz Leśniak, Aleksander Byrski,

Marek Kisiel-Dorohinicki ... 525 Extensible Volunteer Computing Platform

Grzegorz Jankowski, Roman Dȩbski, Aleksander Byrski ... 532 In-Device Coexistence Simulation For Smartphones

Sami Kiminki, Vesa Hirvisalo ... 538 Maximality Semantic For Recursive Petri Nets

Djamel-Eddine Saidouni, Messaouda Bouneb, Jean-Michel Ilié ... 544

XXV

An Integrated Model Of Parallel Processing And PSO Algorithm For Solving Optimum Highway Alignment Problem

Seyed Farzan Kazemi, Yousef Shafahi ... 551 A Discrete-Time Queueing System With Different Types Of Displacement

Ivan Atencia, Inmaculada Fortes, Sixto Sánchez, Alexander V. Pechinkin ... 558 Coordinate-Wise Versions Of The Grid Method For The Analysis

Of Intensities Of Non-Stationary Information Flows By Moving Separation Of Mixtures Of Gamma-Distribution

Andrey Gorshenin, Victor Korolev, Victor Kuzmin, Alexander Zeifman ... 565 Modelling Of Statistical Fluctuations Of Information Flows By Mixtures

Of Gamma Distributions

Andrey Gorshenin, Victor Korolev ... 569 Approach For Analysis Of Finite M

/M

/1/R With Hysteric Policy

For Sip Server Hop-By-Hop Overload Control

Alexander V. Pechinkin, Rostislav V. Razumchik ... 573 Design And Software Architecture Of Sip Server

For Overload Control Simulation

Pavel O. Abaev, Yuliya V. Gaidamaka, Konstantin E. Samouylov,

Sergey Ya. Shorgin ... 580 Data Compression And Recovery For Power Consumption

At Specific Time Instances And In Peak Periods

Tetiana Lutchyn, Bernt Lie, Anatoliy Voloshko ... 587 Stationary Characteristics Of Homogenous Geo/Geo/2 Queue With

Resequencing In Discrete Time

Carmine De Nicola, Alexander V. Pechinkin, Rotislav V. Razumchik ... 594 On Convergence Of Random Walks Having Jumps With Finite Variances

To Stable Lévy Processes

Victor Korolev, Vladimir Bening, Lilya Zaks, Alexander Zeifman ... 601 On M

/M

/S Type Queue With Group Services

Alexander Zeifman, Yakov Satin, Galina Shilova, Victor Korolev,

Vladimir Bening, Sergey Ya. Shorgin ... 604 Criteria On Statistically Defined Bans

Alexander A. Grusho, Nick A. Grusho, Elena E. Timonina ... 610

XXVI

Discrete Event Modelling and Simulation in

Logistics, Transport and Supply Chain Management

Simulation Improves University Campuses Bus Service

Bai Zou, Xiaofan Hu, Ju Xiong, Mingdi You, Edward J. Williams ... 615 Simulating Dynamic Dependencies And Blockages In

In-Plant Milk-Run Traffic Systems

Tobias Staab, Eva Klenk, Willibald A. Günthner ... 622 Warehouse Simulation Through Model Configuration

Jacques Verriet, Roelof Hamberg, Jurjen Caarls, Bruno van Wijngaarden ... 629 Grouping Logistics Objects For Mesoscopic Modeling And Simulation

Of Logistics Systems

Markus Koch, Juri Tolujew ... 636 E-Learning Based Competence Development In Logistics Software Application For Simulation And Visualization

Gaby Neumann ... 644 A Novel, Broadcasting-Based Algorithm For Vehicle Speed Estimation

In Intelligent Transportation Systems Using Ad-hoc Networks

Boyan Petrov, Evtim Peytchev ... 650 Logistic Modelling Of Order Realization In The Complex Parallel

Manufacturing System

Bronislav Chramcov, Robert Bucki ... 657 Simulation Model Of Current Stock Of Divisible Products In ExtendSim

Environment

Eugene Kopytov, Aivars Muravjovs ... 664

XXVII

Policy Modelling

Domain-Specific Languages For Agile Urban Policy Modelling

Michel Krämer, David Ludlow, Zaheer Khan ... 673 Multi-Model Ecologies For Addressing Multi-Scale, Multi-Perspective

Policy Problems

L. A. Bollinger ... 681 Simulating The Cost Of Social Care In An Ageing Population

Eric Silverman, Jason Hilton, Jason Noble, Jakub Bijak ... 689 Traceability In Evidence-Based Policy Simulation

Ulf Lotzmann, Maria A. Wimmer ... 696

Modelling and Simulation in Robotic Applications

Dynamic Modelling Of The “Searazor”- An Interdisciplinary Marine Vehicle For Ship Hull Inspection And Maintenance

Cong Liu, Eilif Pedersen, Vilmar Æsøy, Hans Petter Hildre,

Houxiang Zhang ... 705 Automatic Map Creation For Environment Modelling In Robotic Simulators

Thomas Wiemann, Kai Lingemann, Joachim Hertzberg ... 712 Thrust Analysis On A Single-Drive Robotic Fish With An Elastic Joint

Yicun Xu, Dongchen Qin, Cong Liu, Houxiang Zhang ... 719 Pitching Stability Simulation Of A Bionic Cownose Ray

Yueri Cai, Jun Gao, Shusheng Bi, Cong Liu, Houxiang Zhang ... 726 Jerk Bounded Trajectory Planning For Non-Holonomic Mobile Manipulator

Atef A. Ata, Amr El Zawawi, Mostafa A. E. Razek ... 733

XXVIII

Simulation and Optimization

Simulation Based Clearing Functions For A Model Of Order Release Planning Frederick Lange, Frank Herrmann, Thorsten Claus ... 741 Dynamic Behavior Of Supply Chains

Hans-Peter Barbey ... 748 Combining An Evolutionary Algorithm With The Multilevel Paradigm

For The Simulation Of Complex System

Noureddine Bouhmala, Karina Hjelmervik, Kjell Ivar Øvergård ... 753 A Comprehensive Formulation For Railroad Blocking Problem

Reza Mohammad Hasany, Yousef Shafahi, Seyed Farzan Kazemi ... 758 Selection Of Synchronous Reactive Frequency Converter’s Secondary

Windings Parameters And Optimization Of Rotors Geometrical Dimensions To Ensure Highest Increased Frequency EMF Induction

Aleksandrs Mesnajevs, Andrejs Zviedris, Elena Ketnere ... 764 Bifurcation Model Of Successions In Ecosystems

Serge V. Chernyshenko, Roman V. Ruzich ... 769 Simulation Based Priority Rules For Scheduling Of A Flow Shop With

Simultaneously Loaded Stations

Frank Herrmann ... 775 Analysis Of Backtracking In University Examination Scheduling

Siti Khatijah Nor Abdul Rahim, Andrzej Bargiela, Rong Qu ... 782 Artificial Bee Colony Algorithm For Power Plant Optimization

Friedrich Biegler-König ... 788 Simulation Of Robust Master Production Scheduling In An Industrially

Relevant Planning Environment

Julian Englberger, Frank Herrmann, Thorsten Claus ... 794 A Sustainable Model For Optimal Dynamic Allocation

Of Patrol Tugs To Oil Tankers

Brice Assimizele, Johan Oppen, Robin T. Bye ... 801 An Open Source Software Approach To Combine Simulation

And Optimization Of Business Processes

Mike Steglich, Christian Müller ... 808

XXIX

Modelling and Simulation in Computer Vision for Image Understanding

Stereo Vision Auto-Alignment And The Unsupervised Search For Objects Of Interest With Depth Estimation

Ling-Wei Lee, Faeznor Diana binti Zainordin ... 817 Isogeometric Analysis For Dynamic Model Simulation

Huabin Yin, Qiu Guan, Shengyong Chen ... 824 Self-Adaptive Matching In Local Windows For Depth Estimation

Haiqiang Jin, Sheng Liu, Xuhua Yang, Shengyong Chen ... 831 Image Super-Resolution Reconstruction Using Map Estimation

Xin-Long Lu, Shengyong Chen, Xin Wang, Sheng Liu, Chunyan Yao,

Xianping Huang ... 838 Kernel-Based Manifold-Oriented Stochastic Neighbor Projection Method

Jianwei Zheng, Hong Qiu, Qiongfang Huang, Wanliang Wang, Xinli Xu ... 843 Discrete Point Cloud Filtering And Searching Based On VGSO Algorithm

Fengjun Hu, Yanwei Zhao, Wanliang Wang, Xianping Huang ... 850 Improved Particle Swarm Optimization For Traveling Salesman Problem

Xinli Xu, Xu Cheng, Zhong-Chen Yang, Xuhua Yang,

Wanliang Wang ... 857

Simulation and Computational Neuroscience

The Dynamic Connectome: A Tool For Large-Scale 3D Reconstruction Of Brain Activity In Real-Time

Xeryes D. Arsiwalla, Alberto Betella, Enrique Martinez, Pedro Omedas,

Riccardo Zucca, Paul F.M.J. Verschure ... 865 On Spatio-Temporal Patterns In Two-Layer Ring Topology Neural Fields

Fayssa Salomon, Evan C. Haskell ... 870

Orally presented in this track ... 877 Modeling What You Can Measure In The Brain With Modern Multielectrodes

Gaute Einevoll

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Stable Grid Cells Are Generated From Inhibitory Networks Aree Witoelar

A Model For The Development Of Grid Cells Yasser Roudi

Mean Field Theory For Network Inference With Stochastic Hidden Units John Hertz, Yasser Roudi, Joanna Tyrcha, Benjamin Dunn

Learning In Restricted Boltzmann Machine Bjorn Juel

Statistical Modeling Of Multi-Neuronal Recordings From The Entorhinal Cortex Marie Morreaunet

Simulation in Social Interaction

When Competition Is Pushed Too Hard. An Agent-Based Model Of Strategic Behaviour Of Referees In Peer Review

Juan Bautista Cabotà, Francisco Grimaldo, Flaminio Squazzoni ... 881 Impact Of Homophily On Diffusion Dynamics Over Social Networks

Mustafa Yavaş, Gönenç Yücel ... 888 How Many Parameters To Model States Of Mind?

Krysztof Kulakowski, Piotr Gronek, Antoni Dydejczyk... 895 Multi-Patch Cooperative Specialists With Tags Can Resist Strong Cheaters

Bruce Edmonds ... 900 On The Basic Binding Structure Of A Basic Interaction Scheme

Antônio Carlos da Rocha Costa ... 907

Author Index ... 915

ECMS 2013

SCIENTIFIC PROGRAM

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2

Plenary Talks

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Coordinated learning of grid cell and place cell spatial and temporal properties:

Multiple scales, attention, and oscillations Stephen Grossberg

Center for Adaptive Systems, Graduate Program in Cognitive and Neural Systems, Center for Computational Neuroscience and Neural Engineering,

Department of Mathematics, Boston University, Boston, MA 02215 [email protected], http://cns.bu.edu/~steve

How do grid cells and place cells arise through development and learning? Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis.

The GridPlaceMap neural model shows how grid cells and place cells may develop in a hierarchy of self-organizing maps. In this conception, grid cells and place cells are learned spatial categories in these maps. The model responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors.

Homologous self-organizing map laws for grid cell and place cell learning. The grid cell and place cell self-organizing maps both obey the same laws, and both amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The different receptive field properties emerge because they experience different input sources. The place cells learn from the developing grid cells of multiple scales that input to them. The grid cells learn from stripe cells of multiple scales that input to them, each with a different directional selectivity. The name “stripe cell” acknowledges that the spatial firing pattern of each such cell exhibits parallel stripes as the environment is navigated. Burgess and his colleagues introduced an analogous concept of “band cells”, but they are formed by the mechanism of oscillatory interference.

Grid and place cell learning occurs in models that are built up from either rate-based or spiking neurons. The results using spiking neurons build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models into models whose cells obey spiking dynamics. Remarkably, the spiking model continues to exhibit key analog properties of the data. New properties also arise in the spiking model, including the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation.

Learning the dorsoventral gradient of receptive field sizes and oscillation frequencies. Both the spatial and temporal properties of grid cells vary along the dorsoventral axis of the medial entorhinal cortex. In vitro recordings of medial entorhinal layer II stellate cells have revealed subthreshold membrane potential oscillations (MPOs) whose temporal periods, and time constants of excitatory postsynaptic potentials (EPSPs), both increase along

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this axis. Slower (faster) subthreshold MPOs and slower (faster) EPSPs correlate with larger (smaller) grid spacings and field widths. The self-organizing map model simulates how the anatomical gradient of grid spatial scales can be learned by cells that respond more slowly along the gradient to their inputs from stripe cells of multiple scales. The model cells also exhibit MPO frequencies that covary with their response rates, and exhibit some properties of modular organization of the different spatial scales. The gradient in intrinsic rhythmicity is thus not compelling evidence for oscillatory interference as a mechanism of grid cell firing.

Homologous spatial and temporal mechanisms: Neural relativity. This spatial gradient mechanism is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections that was proposed in the 1980s.

Such adaptively timed learning has simulated data about the role of hippocampus in supporting learning that bridges temporal gaps, such as occurs during trace conditioning and delayed matching-to-sample. This type of "spectrally timed learning" has Weber Law properties that have been confirmed by recent experiments that have discovered "time cells"

in the hippocampus. Spatial and temporal representations may hereby arise from homologous mechanisms, thereby embodying a mechanistic “neural relativity” that may clarify how episodic memories are learned.

Homologous processing of angular and linear velocity path integration inputs. The inputs that drive the initial development of grid cells and place cells are angular and linear velocity signals that are activated by an animal's navigational movements. The model proposes that both angular and linear velocity signals are processed by ring attractor neural circuits. Angular velocity signals are proposed to be processed by ring attractors that are composed of head direction cells, whereas linear velocity signals are proposed to be processed by ring attractors that are composed of stripe cells. The outputs of head direction cells modulate the linear velocity signals to multiple directionally-selective stripe cell ring attractor circuits. This modulation is sensitive to the cosine of the difference between the current heading direction of movement and the ring attractor’s directional preference. Each stripe cell ring attractor is sensitive to a different direction and spatial scale. Stripe cells are the individual cells within each such ring attractor circuit and are activated at different spatial phases as the activity bump moves across their ring locations. They may be activated periodically as the activity bump moves around the ring attractor more than once in response to the navigational movements of the animal.

The model’s assumption that both head direction cells and stripe cells are computed by ring attractors that drive grid and place cell development is consistent with data showing that adultlike head direction cells already exist in parahippocampal regions of rat pups when they actively move out of their nests for the first time at around two weeks of age.

Stable learning, attention, realignment, and remapping. Place cell selectivity can develop within seconds to minutes, and can remain stable for months. The hippocampus needs additional mechanisms to ensure this long-term stability. This combination of fast learning and stable memory is often called the stability-plasticity dilemma. Self-organizing maps are themselves insufficient to solve the stability-plasticity dilemma in environments whose input patterns are dense and are non-stationary through time, as occurs regularly during real-world navigation. However, self-organizing maps augmented by learned top- down expectations that focus attention upon expected combinations of features can do so.

Adaptive Resonance Theory, or ART, proposes how to dynamically stabilize the learned categorical memories of self-organizing maps. Experimental data about the hippocampus from several labs are compatible with the predicted role of top-down expectations and attentional matching in memory stabilization. These experiments clarify

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how cognitive processes like attention may play a role in entorhinal-hippocampal spatial learning and memory stability. The proposed mechanism of top-down attentional matching may also help to clarify data about grid and place cell remapping and alignment of grid orientations.

Beta, gamma, and theta oscillations. Within ART, a sufficiently good match can trigger fast gamma oscillations that enable spike-timing dependent plasticity to occur, whereas a big enough mismatch can trigger slow beta oscillations that do not. Such beta oscillations occur in hippocampus during the learning of novel place cells, and have the properties expected when mismatches occur and receptive field refinements are learned. Beta oscillations also occur at the expected times in visual cortex and in the frontal eye fields during shifts in spatial attention. Thus, the match/mismatch dynamics leading to gamma/beta oscillations seem to occur in multiple brain systems.

The theta rhythm has been associated with properties of spatial navigation, as has firing of entorhinal grid cells. Recent experiments have reduced the theta rhythm by inactivating the medial septum (MS) and demonstrated a correlated reduction in the hexagonal spatial firing patterns of grid cells. These results, along with properties of intrinsic membrane potential oscillations in slice preparations of entorhinal cells, have been proposed to support an oscillatory interference model of grid cells. Our self-organizing map model of grid cells can explain these data without invoking oscillatory interference. In particular, the adverse effects of MS inactivation on grid cells can be understood from how the concomitant reduction in cholinergic inputs may increase conductances of leak potassium and slow and medium after-hyperpolarization channels.

Model parsimony. Our emerging neural theory of spatial and temporal processing in the entorhinal-hippocampal system exhibits a remarkable parsimony and unity in at least three ways: It proposes that similar ring attractor mechanisms compute the linear and angular path integration inputs that drive map learning; that the same self-organizing map mechanisms can learn grid cell and place cell receptive fields, despite their dramatically different appearances; and that that the dorsoventral gradient of multiple scales and modules of spatial learning through the medial entorhinal cortex to hippocampus may use mechanisms that are homologous to mechanisms earlier proposed for temporal learning through the lateral entorhinal cortex to hippocampus ("neural relativity"), as reflected by data about trace conditioning, delayed matching-to-sample, and "time cells". This mechanistic homolog clarifies why both spatial and temporal processing occur in the entorhinal-hippocampal system and why episodic learning may be supported by this system. No less striking is the fact that both grid cells and place cells can develop by detecting, learning, and remembering the most frequent and energetic co-occurrences of their inputs, properly understood. This co- occurrence property is naturally computed in response to data, such as navigational signals, that take on contextual meaning through time.

Supported in part by the SyNAPSE program of DARPA (HR0011-09-C-0001).

Modeling References (see http://cns.bu.edu/~steve)

Fortenberry, B., Gorchetchnikov, A. and Grossberg, S. (2012). Learned integration of visual, vestibular, and motor cues in multiple brain regions computes head direction during visually-guided navigation. Hippocampus, 22, 2219-2237.

Gorchetchnikov, A., and Grossberg, S. (2007). Space, time, and learning in the hippocampus:

How fine spatial and temporal scales are expanded into population codes for behavioral control. Neural Networks, 20, 182-193.

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Grossberg, S. (2009). Beta oscillations and hippocampal place cell learning during exploration of novel environments. Hippocampus, 19, 881-885.

Grossberg, S. (2012). Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world. Neural Networks, 37, 1-47.

Grossberg, S. and Merrill, J.W.L. (1992). A neural network model of adaptively timed reinforcement learning and hippocampal dynamics. Cognitive Brain Research, 1, 3-38.

Grossberg, S. and Merrill, J.W.L. (l996). The hippocampus and cerebellum in adaptively timed learning, recognition, and movement. Journal of Cognitive Neuroscience, 8, 257-277.

Grossberg, S., and Pilly, P.K. (2012). How entorhinal grid cells may learn multiple spatial scales from a dorsoventral gradient of cell response rates in a self-organizing map.

PLoS Computational Biology, 8(10): 31002648. Doi:10.1371/journal.pcbi.1002648.

Grossberg, S. and Versace, M. (2008). Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Research, 1218, 278-312.

Grossberg, S. and Schmajuk, N.A. (1989). Neural dynamics of adaptive timing and temporal discrimination during associative learning. Neural Networks, 2 , 79-102.

Mhatre, H., Gorchetchnikov, A., and Grossberg, S. (2012). Grid cell hexagonal patterns formed by fast self-organized learning within entorhinal cortex. Hippocampus, 22, 320-334.

Pilly, P.K., and Grossberg, S. (2013). How do spatial learning and memory occur in the brain? Coordinated learning of entorhinal grid cells and hippocampal place cells.

Journal of Cognitive Neuroscience, 24, 1031-1054.

Pilly, P.K., and Grossberg, S. (2012). How reduction of theta rhythm by medium septum inactivation may disrupt periodic spatial responses of entorhinal grid cells by reduced cholinergic transmission. Submitted for publication.

Pilly, P.K., and Grossberg, S. (2013). Spiking neurons in a hierarchical self-organizing map model can learn to develop spatial and temporal properties of entorhinal grid cells and hippocampal place cells. PLoS ONE, in press.

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Brain maps for space

May-Britt Moser,

Norwegian University of Science and Technology Kavli Institute for Systems Neuroscience and

Centre for Neural Computation, Trondheim

Norway

27th European Conference on Modelling and Simulation (ECMS) Ålesund, May 27 to 30th, 2013

The

brain controls spatial navigation in mammals by activating functionally specialized cell types in the medial temporal lobe. A key component of the spatial mapping system is the place cell, located in the hippocampus. These cells – discovered by O´Keefe and Dostrovsky in 1971 - are active only when the animal is entering a specific location in the environment. I will describe the discovery of another component of the mammalian spatial mapping system – the grid cell – which we found upstream of the hippocampus, in the medial entorhinal cortex, in 2005. Grid cells are activated whenever an animal enters locations that are distributed in a spatially periodic pattern across the environment. The repeating unit of the grid pattern much is an equilateral triangle. Grid cells are co- localized with head direction cells and border cells, which contain information about the direction in which the animal is moving and the boundaries of the environment. Despite the discovery of several elements of the mammalian spatial map, the interaction between the components is poorly

understood. We addressed this question first by using optogenetics together with

electrophysiologal recordings of cells in the entorhinal cortex. Hippocampal neurons were infected with an adenoassociated virus carrying genes for a peptide tag that can be visualized by fluroescent antibodies as well as the light-sensitive cation channel channelrhodopsin-2 (ChR2). The virus was engineered to enable retrograde transport through axons of cells with projections into the hippocampus. Infected entorhinal cells were detected by local flashes of light. Channelrhodopsin- expressing cells responded with a short and constant latency to the light. All cell types in the entorhinal cortex were found to respond to the light, suggesting that place signals may be generated in the hippocampus by convergence of signals from all these entorhinal cell types. In addition to discussing the transformation of entorhinal to hippocampal spatial signals, I will devote a part of my talk to asking how the grid-cell network is intrinsically organized. To adress this question we used multi-channel recording from a much larger number of cells than recorded ever before in individual animals. Grid cells were found to cluster into a small number of modules with distinct grid scales, grid orientations and grid asymmetries, as well as distinct patterns of temporal organization. The different modules responded independently to changes in the geometry of the environment.

The existence of distinct and independent modules or grid maps makes entorhinal maps different from the many other sensory cortices where functions tend to be more graded and continous. This is in agreement with the suggestion that the grid map is a product of self- organizing network dynamics rather than specificity in the input. Because the crystal-like structure of the grid pattern is generated within the brain, not depending on specific sensory inputs, we are confronted with an unique situation in which we, by trying to understand how the grid pattern is formed, may obtain insights into how patterns are formed in the mammalian cortex.

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Adaptive Model Theory: A History

Megan D. Neilson & Peter D. Neilson

Our accompanying presentation overviews the constructs of Adaptive Model Theory, a computational account of human movement control that has evolved over a research lifetime that began in the 1950s. Its origins are in the fascination of two young people with the prospect of applying their training in physics, mathematics, and engineering principles to the modelling of biological systems. Unlike today, it was an era in which such interdisciplinary work was rare and the path to undertaking it was essentially of one’s own making. In this presentation we explore some of the history of that journey. There was the getting of technical jobs in UNSW’s newly established medical school to provide a gateway to the biological world; the finding of labs where “way-out” ideas were tolerated, if not entirely understood; and the support for part-time graduate research. Not to mention the finding of unmeetable mentors accessible only via the literature.

Experimental work began with studies (without benefit of automated A-D conversion) into investigating the human tonic stretch reflex, not during rest as was usually done, but by modelling the input-output characteristics during voluntary contraction. From these followed a string of experimental investigations to investigate movement disorders, cerebral palsy in particular, but also Parkinson’s disease, stroke, cerebellar disorder, and stuttering. At the time the latter was still widely believed to be psychological in origin. The voluntary movement tracking experiments carried out to model the input-output characteristics of the auditory- motor loop in stuttering and non-stuttering subjects gave strong credence to the now-accepted neurological basis of the disorder. Likewise, following on from the studies of reflexes in cerebral palsied subjects, experimental use of tracking paradigms to separate voluntary and involuntary activity in the purposive movements of this group showed a clear result which is now well accepted clinically. Quite apart from unwanted reflex activity, cerebral palsied subjects lack the ability to generate appropriate movements for a voluntary task, despite a cognitive understanding of what is required. In each of the neurological disorders we studied the experimental evidence pointed to deficits in the formation, maintenance and/or selection of the adaptive sensory-motor models underlying the forward control of voluntary movement.

Hence enter Adaptive Model Theory and the quest for a more general understanding of how the brain achieves control of the inherently nonlinear, nonstationary, uncertain, redundant, unstable system that implements human movement. In parallel with the theoretical

development came more experiment, now mostly with normal subjects. This included examination of control-display compatibility in tracking, acquisition of tracking skill with practice, tracking with differing degrees of freedom, tracking with unusual systems that variously involved gain change, cross-coupling and instability, as well as investigations of predictability in tracking and the response planning strategies that apply. And of course there were the simulations that necessarily accompany the experimental findings and so guide the theoretical evolution. This ultimately has encompassed an account of motor development from the foetus to the mature system. So where now? It’s a long way today from recognising that we can learn about biological systems by modelling their inputs and outputs. A great deal has been done, and not least in understanding the principles of sensory-motor control.

But it’s far from done yet. The mathematics of Riemannian geometry currently offers a new window into the planning of responses of complex systems. Therein lies a future

understanding of human movement and its disorders.

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