Evolving principes of artificial neural design Dennis G. Wilson To cite this version: Dennis G. Wilson. Evolving principes of artificial neural design. Artificial Intelligence [cs.AI]. Uni- versité Paul Sabatier - Toulouse III, 2019. English. NNT : 2019TOU30075. tel-02930188 HAL Id: tel-02930188 https://tel.archives-ouvertes.fr/tel-02930188 Submitted on 4 Sep 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE En vue de l’obtention du DOCTORAT DE L’UNIVERSITÉ DE TOULOUSE Délivré par l'Université Toulouse 3 - Paul Sabatier Présentée et soutenue par Dennis WILSON Le 4 mars 2019 Évolution des principes de la conception des réseaux de neurones artificiels Ecole doctorale : EDMITT - Ecole Doctorale Mathématiques, Informatique et Télécommunications de Toulouse Spécialité : Informatique et Télécommunications Unité de recherche : IRIT : Institut de Recherche en Informatique de Toulouse Thèse dirigée par Hervé LUGA et Sylvain CUSSAT-BLANC Jury M. Marc Schoenauer, Rapporteur M. Keith Downing, Rapporteur Mme Una-May O'Reilly, Examinatrice Mme Sophie Pautot, Examinatrice Mme Anna Esparcia-Alcázar, Examinatrice Mme Emma Hart, Examinatrice M. Hervé LUGA, Directeur de thèse M. Sylvain Cussat-Blanc, Co-directeur de thèse Evolving Principles of Artificial Neural Design Dennis G. Wilson February 28, 2019 2 Abstract The biological brain is an ensemble of individual components which have evolved over millions of years. Neurons and other cells interact in a complex network from which intelligence emerges. Many of the neural designs found in the biological brain have been used in computational models to power artificial intelligence, with modern deep neural networks spurring a revolution in computer vision, machine translation, natural language processing, and many more domains. However, artificial neural networks are based on only a small subset of biological functionality of the brain, and often focus on global, homogeneous changes to a system that is complex and locally heterogeneous. In this work, we examine the biological brain, from single neurons to networks capable of learning. We examine individually the neural cell, the formation of connections between cells, and how a network learns over time. For each component, we use artificial evolution to find the principles of neural design that are optimized for artificial neural networks. We then propose a functional model of the brain which can be used to further study select components of the brain, with all functions designed for automatic optimization such as evolution. Our goal, ultimately, is to improve the performance of artificial neural networks through inspiration from modern neuroscience. However, through evaluating the bio- logical brain in the context of an artificial agent, we hope to also provide models of the brain which can serve biologists. 3 Résumé Le cerveau biologique est composé d’un ensemble d’éléments qui évoluent depuis des mil- lions d’années. Les neurones et autres cellules forment un réseau complexe d’interactions duquel émerge l’intelligence. Bon nombre de concepts neuronaux provenant de létude du cerveau biologique ont été utilisés dans des modèles informatiques pour développer les algorithmes dintelligence artificielle. C’est particulièrement le cas des réseaux neuronaux profonds modernes qui révolutionnent actuellement de nombreux domaines de recherche en informatique tel que la vision par ordinateur, la traduction automatique, le traitement du langage naturel et bien d’autres. Cependant, les réseaux de neurones artificiels ne sont basés que sur un petit sous- ensemble de fonctionnalités biologiques du cerveau. Ils se concentrent souvent sur les fonctions globales, homogènes et à un système complexe et localement hétérogène. Dans cette thèse, nous avons d’examiner le cerveau biologique, des neurones simples aux réseaux capables d’apprendre. Nous avons examiné individuellement la cellule neuronale, la for- mation des connexions entre les cellules et comment un réseau apprend au fil du temps. Pour chaque composant, nous avons utilisé l’évolution artificielle pour trouver les principes de conception neuronale qui nous avons optimisés pour les réseaux neuronaux artificiels. Nous proposons aussi un modèle fonctionnel du cerveau qui peut être utilisé pour étudier plus en profondeur certains composants du cerveau, incluant toutes les fonctions conçues pour l’optimisation automatique telles que l’évolution. Notre objectif est d’améliorer la performance des réseaux de neurones artificiels par les moyens inspirés des neurosciences modernes. Cependant, en évaluant les effets biologiques dans le contexte d’un agent virtuel, nous espérons également fournir des modèles de cerveau utiles aux biologistes. 4 Acknowledgements A thesis can sometimes appear a solitary endeavor and is certainly a reflection of the au- thor’s interests, methods, and understandings. In truth, this thesis has been anything but solitary, with numerous actors influencing not only the work presented in this thesis, but also myself, and my own interests, methods, and understandings. I want to acknowledge a select few, although many others remain unacknowledged but greatly appreciated. My advisors, Sylvain Cussat-Blanc and Hervé Luga, have shaped, supported, and challenged every idea in this document, working with me tirelessly to guide my sometimes circuitous exploration of interests. They have also supported and challenged me as a person, guiding my growth and change these past three years. It isn’t simple moving to a new continent and adapting to a new university, culture, and bureaucracy, and I’ve only made it to this final stage of my thesis thanks to their extensive and comprehensive support. Along the way, I was fortunate to gain another advisor in all but name, Julian Miller. He has been a source of insight in our collaborations, and his passion for researching interesting topics, irrespective of their current difficulty or popularity, has inspired me and encouraged my own research directions. The jury of this thesis have all also influenced it and me in various ways. Keith Downing’s book, Intelligence Emerging, set the direction for much of this thesis and encouraged my interest in artificial life. My first experience with live neurons was in Sophie Pautot’s lab, where I learned how much of a mystery neurons still are. The works of Marc Schoenauer, Emma Hart, and Anna Esparcia have all inspired and informed me, and a motivation to see and maybe impress them has pushed a number of the GECCO articles in this thesis through to completion. Finally, none of this would have happened without Una-May O’Reilly, who took in a somewhat lost sophomore, showed me the marvels of bio-inspired computing, and encouraged me to pursue a PhD in this field. To all of the above, I express my deep gratitude for their impact on this thesis, whether direct or indirect, and on me. I can only hope to someday impact the research and life of another as they have mine. 5 6 Contents 1 Introduction 11 1.1 The brain as a model .............................. 14 1.2 Evolving emergent intelligence ......................... 15 1.3 Organization of the thesis ........................... 17 2 Background 19 2.1 Neural cell function ............................... 21 2.1.1 Biological neural models ........................ 22 2.1.2 Activation functions .......................... 24 2.1.3 Other cell behavior in the brain .................... 26 2.2 Neural connectivity ............................... 26 2.3 Learning in neural networks .......................... 29 2.3.1 Spike Timing Dependent Plasticity .................. 30 2.3.2 Gradient Descent and Backpropagation ................ 32 2.4 Evolutionary computation ........................... 33 2.4.1 Evolutionary strategies ......................... 34 2.4.2 Genetic Algorithms ........................... 34 2.4.3 Genetic Programming ......................... 36 2.5 Evolving artificial neural networks ....................... 36 2.6 Objectives of the thesis ............................. 38 3 Evolving controllers 41 3.1 Artificial Gene Regulatory Networks ..................... 42 3.1.1 AGRN applications ........................... 43 3.1.2 AGRN overview ............................. 45 3.1.3 AGRN dynamics ............................ 47 3.1.4 AGRN experiments ........................... 50 3.1.5 AGRN results .............................. 52 3.2 Cartesian Genetic Programming ........................ 56 7 Contents 3.2.1 CGP representation ........................... 59 3.2.2 Playing games with CGP ........................ 60 3.2.3 Positional Cartesian Genetic Programming .............. 65 3.2.4 Genetic operators for CGP ....................... 66 3.2.5 CGP experiments ............................ 68 3.2.6 CGP method comparison ........................ 69 3.2.7 CGP parameter study ......................... 71 3.3 Conclusion .................................... 74 4 Evolving neural cell function 77 4.1 Spiking neural activation functions ...................... 78 4.2 Neural network model ............................. 81