René Doursat
 PhD, Habil.

Professor of Complex Systems & Deputy
   Head, Informatics Research Centre,
   School of Computing, Math & Digital Tech,
   Manchester Metropolitan University, UK

Research Affiliate, BioEmergences Lab,
   CNRS (USR3695), Gif s/Yvette, France

Steering Committee & Fmr. Director,
   Complex Systems Institute, Paris (ISC)

Officer (Secretary), Board of Directors,
   International Society for Artificial Life



email: R.Doursatmmu.ac.uk





Books

Growing Adaptive
Machines



Springer 2014
Morphogenetic
Engineering



Springer 2012
Cognitive
Morphodynamics



Peter Lang 2011
Edited Proceedings

Artificial Life
ALife'14, ECAL'15





MITPress 2014,2015
Evolution. Comp.
GECCO'12, '13





ACM 2012,2013
Artificial Life
ECAL'11



MITPress 2011
Swarm Intell.
ANTS'10



Springer 2010
IT Revolutions
ICST'08



Springer 2009


Home Page
Research
   • Morphogenesis
   • Morphogenetic
     Engineering
   • Neuroscience
   • Evolution & Ecology
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Education, Career

Research in Complex Systems  
In the vast land of complex systems, I commute back and forth between computational biology and bio-inspired computing. On the way, I founded the field of morphogenetic engineering (ME), which explores new methodologies to model and create complex architectures that self-organize from a swarm of heterogeneous agents, in particular by development. Such emergent structures can be modular robots, synthetic organisms, or large autonomic networks of computing devices. ME could also explain brain representations based on dynamic "neural shapes" in phase space, formed by myriads of correlated spikes. Additionally, I am interested in the evolutionary mechanisms leading to diversity, and how they can help us understand and automate the design of ME systems.
Complex systems are characterized by the self-organization of small, repeated elements into large-scale patterns. They are abundant in our environment, whether as natural structures (physical, chemical, biological) or spontaneously emerging human organizations (cities, markets, Internet). Such decentralized, self-organized and unplanned systems are probably the most common and efficient type of systems. It is centrally designed systems that are unique and costly to build, as they require another intelligent system (human or human-made machine, in a recursive fashion) to exist and operate. In fact, "complex" systems might well turn out to be the simplest.

In this context, natural systems can play an important role in providing a powerful source of inspiration toward new technologies. Understanding natural self-organization could help create a new generation of artificial systems with properties still largely absent from traditional engineering, such as decentralization, autonomy and adaptivity. Toward this goal, my research is positioned at the interface between the science and the engineering of complex systems, focusing on biological topics and bio-inspired principles. It tries to cover both the computational modeling and simulation of biological self-organization (such as development, neural networks, or population dynamics) and the engineering of "intelligent" devices inspired by these phenomena (such as swarm/modular robotics, distributed computing, or language processing):

  • Theoretical & Computational Biology: The pervasiveness of complex systems is most striking in biology, at all scales of resolution from molecules and cells to organisms and populations. Agent-based, computational modeling and simulation is a powerful tool to explore biological complex systems, including gene regulation, morphogenesis, neural networks, and evolution.

  • Bio-Inspired Computing & Bioengineering: In parallel to biological modeling, another important goal is to explore artificial systems consisting of a multitude of micro-programmed elements interacting locally. The motivation is to obtain new architectures and powerful functionalities from "bottom-up" self-organization, ones that cannot be directly designed "top-down" by engineers. ← Less
Morphogenesis
Computational models of collective cell behavior in 2D/3D, such as organism development (embryogenesis), tumor growth, or bacterial mats (synthetic biology)
•  MECAGEN – Mechanogenetic Model of Biological Morphogenesis
•  BIOEMERG – BioEmergences: Reconstructing the Physiome of Model Organisms
•  SYNBIOTIC – Synthetic Biological Systems: From Design to Compilation
Morphogenetic Engineering
Designing decentralized, autonomous systems inspired by morphogenesis, with applications in swarm robotics, distributed software, and ICT networks or power grids
•  EMBENG – Embryomorphic Engineering (2D)
•  MAPDEVO – Modular Architecture by Programmable Development (3D)
•  PROGNET – Network Growth by Programmable Attachment (nD)
Neuroscience
Mesoscopic emergence, interaction and composition of spatiotemporal patterns of activity and connectivity in large-scale spiking neural networks
•  SYNDEVO – Synfire Chain Development and Composition
•  WAVEMAT – Wave-Based Shape Storage and Matching
•  COGMORPH – Morphodynamical Models of Cognitive Linguistics
Evolution & Ecology
Agent-based, grammar-based or genetic programming models of population dynamics combining the short and long time scales of individual interactions and evolution
•  EVOSPACE – Evolutionary Dynamics and Speciation in Space
•  DIVPLANT – Emergent Diversity in Communities of Virtual Plants
•  HETCA – Long-Term Evolutionary Dynamics in Heterogeneous Cellular Automata