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



Growing Adaptive

Springer 2014

Springer 2012

Peter Lang 2011
Edited Proceedings

Artificial Life
ALife'14, ECAL'15

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

ACM 2012,2013
Artificial Life

MITPress 2011
Swarm Intell.

Springer 2010
IT Revolutions

Springer 2009

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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.

In sum, it is as if cognitive science found itself in the same situation as biology a century ago: back then, evolution and genetics were both recognized but still uncorrelated scientifically. It is only later, when DNA, RNA and proteins were discovered, that these two levels became finally linked and united through the Modern Synthesis. In other terms, by elucidating the mesoscopic level of life's complex self-organization (molecular biology), macroscopic phenomena (heredity, speciation) could be explained on the basis of microscopic elements (atoms, molecules; Fig. a).

Likewise, metaphorically speaking, we should ask ourselves what the "molecules" of cognitive science's future Mind-Brain Modern Synthesis will be. In theory, what composite and complex objects could possibly explain the laws of perception and language as an emergence from of elementary neuronal activities? (Fig. b). The structure and properties of cognitive states has long been a subject of debate since the beginnings of modern neuroscience. It is generally assumed that the average firing rate of "cell assemblies" constitute an important part of the neural code. In contrast, following the tracks of my postdoc and thesis advisors, Christoph von der Malsburg (1981) and Elie Bienenstock, I have promoted temporal coding as another important format of representation, one that involves higher-order moments or "correlations" among neuronal activities.

Generally, if xi(t) denotes the time-varying potential of neuron i, the answer could lie in large dynamic groups { x1(t), ..., xn(t) } characterized by spike timing series { t11, t12, t13, ..., tn1, tn2, tn3, ... }, which contain many high-order moments <xi(t) xj(t - τij) xk(t - τik) ...>, typically combinations of synchronized groups (where delays τij = 0) and waves or "rhythms" (where delays τij > 0). Called spatiotemporal patterns (STPs), these reproducible correlations among electric signals must also be supported by underlying regular patterns of connectivity.

Similarly to proteins, STPs could then interact in several ways and assemble ("bind") at several levels, forming a hierarchy of composite structures from simpler ones, like "building blocks" of intelligent behavior (Bienenstock 1996). My own research goal is to outline a new theoretical framework for this type of complex mesoscopic neurodynamics with compositional properties. I have conducted different studies (see below) that all construe the cortical substrate of neuronal units and synaptic contacts as an "excitable medium" and have potential applications to of artificial systems in perceptual, linguistic or behavioral tasks. ← Less


Bienenstock (1996) Composition. In Brain Theory: pp. 269-300. Elsevier.

von der Malsburg (1981) The Correlation Theory of Brain Function. Max-Planck Inst.


Doursat, R. (2013) Bridging the mind-brain gap by morphogenetic "neuron flocking": The dynamic self-organization of neural activity into mental shapes. How Should Intelligence Be Abstracted in AI Research? Technical Report FS-13-02, in AAAI 2013 Fall Symposium Series, November 15-17, Arlington, VA; S. Risi, J. Lehman, J. Clune, eds.: pp. 16-21. ISBN 978-1-57735-640-0. PAPER

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. Experiments in mammalian neocortex have gathered some evidence for these patterns, which were hypothetically named "synfire chains" (Abeles 1982; uniform connection delays, Fig. a) or "synfire braids" (Bienenstock 1995; unequal delays, Fig. b).

We also postulate that synfire patterns might constitute the elementary components or "molecular building blocks" at the mesoscopic level of the mind's symbolic abilities (see Introduction above), in particular the faculty of compositionality at the core of linguistic and perceptual functions (Fig. b-d).

Multiple synfire patterns have the required combinatorial structure to assemble in various ways through wave synchronization and fast synaptic binding (Fig. c). Thus, synfire growth could be only the first step in an infinitely productive "network of networks" hierarchy.

Under the influence of external stimuli, chain motifs dynamically bind into higher structural compositions characterized by complex modes of activity (spatiotemporal patterns; STPs). This model suggests that neocortex is another instance of a "self-made tapestry" (Ball 1999) showing pattern formation, as are many natural complex systems. ← Less


Abeles (1982) Local cortical circuits. Springer.

Ball (1999) The Self-Made Tapestry. Oxford Univ. Press.

Bienenstock (1995) A model of neocortex. Network 6: 179-224.


Doursat, R. & Bienenstock, E. (2006) Neocortical self-structuration as a basis for learning. 5th International Conference on Development and Learning (ICDL 2006), May 31-June 3, 2006, Indiana University, Bloomington, IN. IU, ISBN 0-9786456-0-X. PAPER POSTER

Doursat, R. (1991) Contribution à l'étude des représentations dans le système nerveux et dans les réseaux de neurones formels. PhD Thesis, Université Pierre et Marie Curie (Paris 6). PART 3

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. Accordingly, there are two components in the schematic recognition task studied here: the input image and the stored shape, which rely on a common internal metric formed by accurate spatiotemporal relationships among action potentials.
Based on this setup, I designed a model of dynamical matching between lattices of coupled stochastic oscillators (local excitatory/inhibitory assemblies), behaving like "excitable units" able to spontaneously generate traveling waves. The interaction between input pattern and schema pattern, formally interpreted as a graph-matching task, is implemented through the growth of dynamical links from one pair of STPs to another pair of STPs. ← Less

Doursat, R. (2011) Morphogenetic "neuron flocking": The dynamic self-organization of neural activity into mental shapes. Workshop on Mathematical Models of Cognitive Architectures, December 5-9, 2011, International Center of Mathematics Meetings (CIRM), Université d'Aix-Marseille, France. SLIDES

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? Gestalt-inspired cognitive linguistics (Talmy 2000) has shown that spatial prepositions such as 'in' or 'above' are neutral toward the shape and size of objects. This core invariance of spatial semantics can be referred to as cognitive topology, which is at the same time more flexible (e.g., allowing holes) and more metrically constrained (e.g., limiting distortions) than mathematical topology.

The goal of this study is to understand and reproduce how our cognitive system creates schemas and categories at the interface between vision and language (Regier 1996), in particular its ability to effortlessly map an infinite diversity of visual scenes onto a small set of grammatical elements (Fig. a). Through this challenge we seek to elucidate the fundamental transformation of continuous dynamical stimuli into discrete symbolic contents (Petitot 2003), or ontology, which is emblematic of human intelligence and will be crucial to a reunited, biologically inspired AI.

We suggest that language-specific invariants toward shape, size or other object attributes can be explained using morphological transforms (Fig. b): expansion processes erase image details and create virtual structures (domains, skeletons), which yield a characteristic "signature" for the scene. Such singularities can arise from a large-scale lattice of coupled excitable units exhibiting pattern formation through spatiotemporal order, especially traveling waves. The originality of our approach is thus to embed semantic processing in complex systems, in particular to exploit dynamic pattern formation, or morphodynamics, that can emerge from cellular automata or lattices of coupled oscillatory neurons (Fig. c).

In summary, our work addresses the crucial cognitive mechanisms of spatial schematization and categorization at the interface between vision and language and anchors them to dynamical processes such as activity diffusion or wave propagation. ← Less


Petitot (2003) Morphogenesis of Meaning. Peter Lang.

Regier (1996) The Human Semantic Potential. MIT Press.

Talmy (2000) Toward a Cognitive Semantics. MIT Press.

Selected publications

Doursat, R. & Petitot, J. (2005) Bridging the gap between vision and language: A morphodynamical model of spatial categories. In Proceedings of the 2005 IEEE International Joint Conference on Neural Networks (IJCNN 2005), July 31-August 4, 2005, Montréal, Canada: Vol. 5, pp. 2903-2908. IEEE, ISBN 0-7803-9048-2. PAPER

Petitot, J., in collaboration with Doursat, R. (2011) Cognitive Morphodynamics: Dynamical Morphological Models for Constituency in Perception and Syntax. Peter Lang, ISBN 978-3-0343-0475-7. INTRO