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Neuro-physics: Theoretical approach and modeling of neuronal network cultures

Non linear interactions, information processing and cooperative phenomena underlie the ability of a neuronal network to perform biological computation. In the human brain, each of the 10^11 neurons are connected to a great number of other ones (about in average 7 000 per neuron) ; the signaling is both electrical (propagation of action potentials) and chemical (neurotransmitter release through synapses) and travels along axons in a single direction. Besides the investigation of the cerebral activity in vivo by means of electroencephalography (EEG), magnetoencephalography and Functional Nuclear Magnetic Resonance (NMR), in vitro reconstruction of neuronal networks has turned out to be a powerful tool in investigating fundamental questions about biological computation, mechanisms of cognition and neurodegenerative disorders.

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Two-dimensional neural network
Image of an in vitro grown neuronal network obtained by fluorescence microscopy ; inhibitory neurons appear in red, excitatory neurons in green

Experimentally, such a reconstruction is carried out by seeding dissociated neurons extracted from rat or mouse embryos on a polydimethylsiloxane (PDMS) substrate ; neurites grow in such a way that neurons self-organize after a few days into a network which exhibits a spontaneous bursting activity. When grown on a flat plate, such a network can include up to 500 000 neurons with a typical density of about 5 000 neurons per mm². The neuronal activity can then be recorded either by means of electrical measurements with the help of Multi-electrode arrays (MEA) either by optical measurements involving calcium or voltage dependent fluorescent dyes. Although neuronal networks grown in vitro exhibit connectivity patterns different from the ones observed in vivo, the change in scale required by in vitro studies is unique in the sense that it allows to control the experimental environment, so that specific mechanisms and connectivity geometries can be thoroughly investigated. Extracellular ions concentrations can be varied and even be set outside the ranges allowed in vivo, drugs can be added to control neurotransmitter release,… Our research activity is focused on the following three main directions :

1°/ How can the emergence of collective behaviors in neuronal networks be understood from a statistical physics point of view ? What can be extracted from a model with regards to network topology and neuronal activity ?

2°/ What is the nature of neuronal information processing ? What kind of computation do neuronal networks perform ? Are in vitro neuronal devices able to learn ?

3°/ How do neuronal networks in vitro grow ? How can the directionality of axonal growth be understood and controlled ?


[1] R. Renault, P. Monceau and S. Bottani, Phys. Rev. E. 88, 062134 (Dec. 2013). Memory decay and loss of criticality in quorum percolation.

[2] R. Renault, P. Monceau, S. Bottani and S. Métens, Physica A. 414, 352, (Juil. 2014). Effective non-universality of the quorum percolation model on directed graphs with Gaussian in-degree.

[3] Renaud Renault, Nirit Sukenik, Stéphanie Descroix, Laurent Malaquin, Jean-Louis Viovy, Jean-Michel Peyrin, Samuel Bottani, Pascal Monceau, Elisha Moses, Maéva Vignes, PLoS ONE 10(4) : e0120680. doi:10.1371/journal.pone.0120680, (Apr. 2015). Combining microfluidics, optogenetics and calcium imaging to study neuronal communication in vitro.

[4]S. Métens, P. Monceau, R. Renault, and S. Bottani, Phys. Rev. E 93, 032112, (March 2016). Finite-size effects and dynamics of giant transition of a continuum quorum percolation model on random networks.

[5]Pascal Monceau, Renaud Renault, Stéphane Métens, and Samuel Bottani, Phys. Rev. E 94, 012316 (July 2016) Effect of threshold disorder on the quorum percolation model

Master internships

  • Dec. 2011-Avr. 2012, R. Renault, "Investigation of a quorum percolation model for neuronal networks".
  • May-July 2013, N. Fossey, "Modelling of the collective behavior of neuronal networks : effects of thresholds disorder".
  • May-July 2013, M. Boukhet, "Modelling of the properties of connected clusters in the framework of neuronal networks : quorum percolation."
  • Sept-Dec. 2013, D. Davila, "Investigation of a computational platform for cultures of dissociated neuronal networks simulations".
  • Jan-Mai 2014, V. Lalande, "Modelling of learning processes for in vitro neuronal networks."
  • Jan-Mai 2014, M. Agamennone, "Modeling, simulation and study of two-dimensional dissociated neuronal cultures : growth process, topology, activity".
  • Jan-Mai 2016, M. Ballandras, "Synchronization in neuronal networks".


September 2012-October 2015, Renaud Renault, "Emergent design of neuronal devices".

September 2015- , Tanguy Fardet, "Growth and activity of neural networks : emergence of non-trivial behaviours."

Nom des membres actuels ou anciens

BOTTANI Samuel, METENS Stéphane, MONCEAU Pascal, RENAULT Renaud, FARDET Tanguy

Mot-clé général



Si les informations de cette page sont erronées ou incomplètes (par exemple la liste des thèmes de recherche), merci de contacter l'équipe web (lab-msc-web AT