Computational Neuroscience

Department 5 : Complex systems, artificial intelligence and robotics

Team leader : Bernard Girau
Tél. : +33 3 83 59 20 58
Mail :



The goal of our research is to study the properties and computational capacities of distributed, numerical and adaptative networks, as observed in neuronal systems. In this context, we aim to understand how complex high level properties may emerge from such complex systems including their dynamical aspects. In close reference to our domain of inspiration, Neuroscience, this study is carried out at three scales. (i) At the microscopic level, our approach relies on precise and realistic models of spiking neurons and of the related dynamics, analyzing the neural code in small networks of spiking neurons. (ii) At the mesoscopic level, the characteristics of a local circuit are integrated in a high level unit of computation, i.e. a dynamic neural field. This level of description allows us to study larger neuronal systems, such as cerebral maps, as observed in sensori-motor loops. (iii) At the macroscopic level, the analysis of physiological signals and psychometric data is to be linked to more cognitive and behavioral hints for the analysis of Higher level functions. This is for instance the case with electroencephalographic (EEG) recordings, allowing to measure brain activity, including in brain computer interface paradigms.

Research activities

  • Spiking neurons
  • Dynamic neural fields
  • Higher level functions
  • Embodied and embedded systems


  • GINNet
  • Contributions sur OpenViBE


  • Max Planck Institute (Germany)
  • Ottawa University (Canada)
  • Valparaiso University (Chile)
  • Centre Cinvestav Tamaulipas (Mexico)
  • Jean-Pierre Rospars, INRA  Versailles


Computational Neuroscience, spiking neuron, neural field, neural code, behavioral model, brain-machine interface, brain-inspired hardware, embodiment, emergence, autonomy, learning, cogni- tive tasks


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