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
       SYNDEVO
       WAVEMAT
       COGMORPH
   • Evolution & Ecology
Teaching
Publications
Activities, Grants
Industry
Education, Career

Neuroscience  
Mesoscopic emergence, interaction and composition of spatiotemporal patterns of activity and connectivity in large-scale spiking neural networks
Cognitive science is currently a federation of disciplines—psychology, AI, linguistics, logic, neuroscience, etc.—lacking a unified theory. Cognitive models are broadly divided between a logical paradigm, or "cognitivism", and a dynamical paradigm, or "connectionism". Bridging this gap will require an intermediate or mesoscopic level of description, at a finer granularity than symbols but larger structural complexity than small artificial neural nets.
Logical systems define high-level symbols and formal grammars, but do not have a microstructure needed to explain the fuzzy complexity of perception, memory or learning. Conversely, dynamical systems define functionality as emerging from neural networks and low-level activation equations, but lack a macroscopic level supporting the systematic symbolic composition of language and reasoning. More →
SYNDEVO – Synfire Chain Development and Composition  
Parallel self-organization of connectivity and activity in an initially random spiking neural network, to support a hierarchy of structured representations applied to visual, auditory or linguistic tasks
Striking regularities in the connectivity structure of the visual system and other cortical areas account for their functional specialization. Elie Bienenstock and myself have designed a neural network model that reproduces the development of such regularities as a phenomenon of spatiotemporal pattern formation. We show the spontaneous and simultaneous emergence of regular chains of synaptic connectivity together with a wave-like propagation of neural activity.
Starting from an undifferentiated random state, our dynamical, self-reconfiguring neural network transitions into an ordered regime of activity and connectivity. In this regime, chains sustain and guide waves, which in turn create and reinforce chains. More →
WAVEMAT – Wave-Based Shape Storage and Matching  
Coding visual metric with temporal correlations: a model of deformable template matching based on phase-locking
In this work, started when I was a postdoc with Christoph von der Malsburg and pursued today with my PhD student Carlos Sánchez, I propose that nonzero phase-locking (i.e. delayed correlations) among oscillating units can be exploited to code for metric relationships among the features of an image. If spiking times are interpreted as coordinates, then two spatiotemporal patterns of activity (STPs) can represent a 2D visual pattern.
Visual perception likely involves two processing levels: bottom-up processing, during which features spontaneously group according to low-level cues, and top-down processing, during which global segmentation is completed from above by schemas previously stored in memory. More →
COGMORPH – Morphodynamical Models of Cognitive Linguistics  
Bridging the gap between vision and language by importing complex system modeling into linguistics
How can the same relationship 'in' apply to containers as topologically different as 'box', 'tree' or 'bowl'? The objective of this study is to categorize the infinite diversity of schematic visual scenes into a small set of grammatical elements. To elucidate this "topology of language", I have proposed with Jean Petitot a novel dynamical system approach to cognitive linguistics based on the generation of traveling waves in cellular automata and spiking neural networks.
How can the same linguistic preposition 'in' apply to scenes as different as "the shoe in the box" (small, hollow, closed volume), "the bird in the tree" (large, dense, open volume) or "the fruit in the bowl" (curved surface)? How can language be so insensitive to wide topological and morphological differences among visual percepts? More →