French National Centre for Scientific Research (CNRS)
In a nutshell
Main area of expertise is Neuroscience. Interested in the interaction between glial cells and neurons in generating neuronal network activity and function, and the mechanisms underlying dysfunction in disease states. Use of single cell, network electrophysiological and dynamic fluorescence imaging methods.
Statistical Physics, Computational Neuroscience and Computational Biology, Machine Learning
Societal Affiliations
British Neuroscience Association (BNA) Society for Neuroscience (SFN)
Federation of European Neuroscience (FENS)
Alzheimer’s Society
Research Website
Google Scholar
Ecole Normale Supérieure – Laboratory of Physics
Centre National de la Recherche Scientifique
ORCID: 0000-0002-4459-0204
Professor at the Ecole Polytechnique
Director of Research CNRS 1st class
R.M. is a theoretical physicist, working on the theory, development and applications of statistical physics methods to study computational problems in inference/machine learning and in computational neuroscience. R. Monasson has authored 126 publications (h-index 41, 6550 citations). R.M. was awarded the Bronze Medal from CNRS in 1997, the Lecomte prize from the Académie des Sciences in 2004, and the Langevin prize from the French Physical Society in 2010; he was senior member at the Center for Systems Biology, Institute for Advanced Study in Princeton from 2009 to 2011.
“I find this project fascinating because it is a unique opportunity to build a functional neural network from scratch, and the idea of driving this network to a desired state of connectivity, so that it can accomplish computations, is fascinating!”
Other active projects:
Selected Publications
- Integration and multiplexing of positional and contextual information by the hippocampal network, L. Posani, S. Cocco, R. Monasson, PLoS Computational Biology 14: e1006320 (2018).
- Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings, G. Tavoni, S. Cocco, R. Monasson, J. Comp. Neurosci. 41, 269-293 (2016).
- Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods, S. Cocco, S. Leibler, R. Monasson, Proc. Natl. Acad. Sci. USA 106, 14058 (2009)