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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 (ME),
which explores new methodologies to model and create complex
architectures that self-organize from a swarm of heterogeneous
agents, in particular by .
Such emergent structures can be , , or large of computing devices.
ME could also explain brain representations based on dynamic
in phase space, formed by myriads of correlated spikes. Additionally,
I am interested in the mechanisms leading to
diversity, and how they can help us understand and automate
the design of ME systems.
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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.
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Computational models of collective cell behavior in 2D/3D,
such as organism development (embryogenesis), tumor growth,
or bacterial mats (synthetic biology)
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Designing decentralized, autonomous systems inspired by
morphogenesis, with applications in swarm robotics,
distributed software, and ICT networks or power grids
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Mesoscopic emergence, interaction and composition of spatiotemporal
patterns of activity and connectivity in large-scale spiking neural networks
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Agent-based, grammar-based or genetic programming models of population dynamics
combining the short and long time scales of individual interactions and evolution
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