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The Role of Spike Patterns in Neuronal Information Processing

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The Role of Spike Patterns in Neuronal Information Processing DISS. ETH NO. 16464 The Role of Spike Patterns in Neuronal Information Processing A Historically Embedded Conceptual Clarification A dissertation submitted to the SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH for the degree of Doctor of Sciences (Dr. sc. ETH Z¨urich) presented by Markus Christen Lic. phil. nat., Universit¨at Bern born 07. 01. 1969 accepted on the recommendation of Prof. Dr. Rodney Douglas, Examiner PD Dr. Ruedi Stoop, Co-Examiner, Supervisor Prof. Dr. Michael Hagner, Co-Examiner 2006 ii Author: Markus Christen, lic. phil. nat. Institute of Neuroinformatics Winterthurerstrasse 190 8057 Zurich c 2006 Brainform B¨ozingenstrasse 5, 2502 Biel, Switzerland http://www.brainform.com Print: Printy, Theresienstrasse 69, 80333 M¨unchen Printed in Germany ISBN-10: 3-905412-00-4 ISBN-13: 978-3-905412-00-4 Purchase order: [email protected] This work is subject to copyright. All rights are reserved, whether the whole or part of the mate- rial is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplica- tion of this publication or parts thereof is permitted only with prior permission from the current publishers. No part of this publication may be stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior permission from the publishers. You are not allowed to modify, amend or otherwise change this work without prior consent from the the current publishers. All copyright for this work will remain the property of the author. Acknowledgements Writing a PhD thesis is embedded in a net of relations and conditions. Therefore, I have many thanks to express. First, I’m indebted to my PhD supervisor, PD Dr. Ruedi Stoop. He not only made it possible for me to re-enter science after a longer stay in professional life. He also is convinced of the idea that one should find one’s own topic instead of being assigned to one. He offered help, when it was important, and recalcitrance, when it was necessary in order to sharpen my own ideas. He finally allowed my expeditions into the fields of humanities, which made it possible to combine two cultures in this interdisciplinary PhD, which usually remain separated. Profs. David Gugerli and Michael Hagner were the exponents on the other side of this cultural divide, who provided assistance and many helpful comments. Sincere thanks go to both of them. I was lucky that I was integrated in a small, but busy group of excellent physicists and engineers. We not only shared science, but also life and beer to some degree – an aspect that never should be underestimated. Specific thanks go to Albert Kern for his assistance in understanding the correlation integral, to Thomas Ott for many clusterings and to Jan- Jan van der Vyver for programming, computer, and language help. Various alumni and visitors of the Stoop group and many more people in the Institute of Neuroinformatics could be mentioned as well. I cut short by thanking Profs. Rodney Douglas and Kevan Martin, who made it possible that such an institute can exist. Furthermore, I’m very much obliged to those, who provided data that have been used in this study and made it possible to analyze the tools introduced by this work. My thanks go to Alister Nicol of the Cognitive & Behavioural Neuroscience Lab of the Babraham Institute in Cambridge, Adam Kohn of the Center of Neural Science of the New York University, Valerio Mante and Matteo Carandini of the Smith Kettlewell Eye Research Institute in San Francisco, and Kevan Martin of the Institute of Neuroinformatics. The historical part of this PhD originated in the Max Planck Institute for the History of Science in Berlin. Many thanks go to Profs. Michael Hagner and Hans-Jorg¨ Rhein- berger, who made it possible to join this exciting community from December 2004 to March 2005. I also thank Urs Schoepflin for his helpful comments for my bibliometric studies and the participants of the colloquium of the ‘Abteilung III’ that commented and criticized the first version of this part. I finally thank Robert Ruprecht and Adrian Whatley for proofreading the scientific and the historical part of this thesis. They are certainly not to blame for the remaining mistakes. The most important thanks are always expressed at the end: I thank my parents and my sisters for their existence, and Meret for her love. Markus Christen, November 2005 iii iv Contents Acknowledgements iii Zusammenfassung ix Summary x Statement xii 1 Introductory Remarks 1 1.1 Goals of this Study ................................. 1 1.2 Methodology .................................... 3 1.3 Organization of the Thesis ............................. 3 1.3.1 Main Line of Argumentation ....................... 3 1.3.2 Typographic Structure and Layout .................... 5 1.4 Abbreviations and List of Symbols ........................ 6 1.4.1 Abbreviations . ............................. 6 1.4.2 List of Symbols . ............................. 6 I Historical Roots of the Information Processing Brain 9 2 The Brain on the Cusp of the Information Age 11 2.1 Introduction ..................................... 11 2.1.1 The Information Processing Brain .................... 11 2.1.2 The Historical Context ........................... 13 2.2 Cornerstones of the Information Age ....................... 15 2.2.1 The Conceptualization of Information .................. 15 2.2.2 Information Theory ............................ 16 2.2.3 Cybernetics ................................. 19 2.3 Preconditions for the Information Processing Brain ............... 21 2.3.1 The Scheme for Analysis .......................... 21 2.3.2 The Neuron Doctrine ........................... 23 2.3.3 Spikes and Messages ............................ 25 2.3.4 Measuring Single Fibres .......................... 28 v vi CONTENTS 3 The Birth of the Information Processing Brain 31 3.1 1940-1970: Overview . ............................. 31 3.2 1940-1970: Detailed Analysis ........................... 33 3.2.1 A new Role for the Neuron? ........................ 33 3.2.2 Applying the Information Vocabulary .................. 35 3.2.3 The Emergence of a Toolbox for Spike Train Analysis . ...... 51 3.2.4 The (Random) Network .......................... 53 3.2.5 The Active and Reliable Brain ...................... 56 3.2.6 The Modelling Approach ......................... 61 4 1940 – 1970: The Dynamics of Research 65 4.1 Journals and Publications ............................. 65 4.2 Conferences ..................................... 69 4.3 Protagonists ..................................... 75 4.4 Synopsis: Qualitative and Quantitative Analysis ................ 86 4.4.1 Up to 1940: Preconditions ......................... 86 4.4.2 1940-1970: Main Developments ...................... 88 II Patterns in Neuronal Spike Trains 91 5 Neural Coding and Computation 93 5.1NeuralCoding................................... 93 5.1.1 What is a ‘Neural Code’? ......................... 93 5.1.2 Rate Coding vs. Temporal Coding ....................100 5.1.3 Single Neurons vs. Neuronal Assemblies .................102 5.2 Neural Computation . .............................103 6 Defining Patterns 105 6.1 Characteristics of Patterns .............................105 6.1.1 Defining ‘Spike Pattern’ ..........................105 6.1.2 Types of Events . .............................108 6.1.3 The Stability of Patterns .........................111 6.2 The Background of a Pattern ...........................113 6.2.1 The Significance of Patterns ........................113 6.2.2 The Poisson Hypothesis.........................113 6.2.3 Randomization Methods ..........................116 6.3 Noise and Reliability . .............................119 6.3.1 What is Noise in Neuronal Systems? ...................119 6.3.2 Noise Sources in Neurons .........................119 6.3.3 The Reliability of Neurons .........................122 6.3.4 Can Neurons Benefit from Noise? .....................124 6.4 Functional Aspects of Spike Patterns .......................125 6.4.1 Causing Patterns: Network, Oscillation, Synchronization . ......126 6.4.2 Using Patterns: LTP and Coincidence-Detection ............131 6.5 Spike Patterns: Examples .............................134 CONTENTS vii 6.6TheStoop-Hypothesis...............................137 6.6.1 Neurons as Limit Cycles ..........................138 6.6.2 Locking – The Coupling of Limit Cycles .................139 6.6.3 Emergence of a Coding Scheme ......................141 6.6.4 The Role of Patterns ............................146 6.6.5 The Integral Framework ..........................148 7 Detecting Patterns 151 7.1 The Problem of Pattern Detection ........................151 7.1.1 Outlining the Problem ...........................151 7.1.2 Pattern Detection Methods: Overview ..................152 7.1.3 Spike Train Distance Measures: Overview ................153 7.2 Histogram-based Pattern Detection ........................155 7.2.1 Classes of Histograms ...........................155 7.2.2 1D Interval Histograms ..........................160 7.3 Correlation Integral based Pattern Discovery ..................163 7.3.1 The Correlation Integral ..........................163 7.3.2 Smeared log-log Steps ...........................164 7.3.3 Pattern Length Estimation ........................166 7.4 Template-based Pattern Recognition .......................170 7.4.1 The Template Approach ..........................170
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