Principal investigator : Bruno CESSAC.
On going work : Analyze, at the local network level, the statistical properties of ganglion cells output spike trains, including adaptation mechanisms :
- Biological high-order statistics analysis
- Algorithm performance improving
- Parametric network model estimation
- Analyze, at the local network level, the statistical properties of ganglion cells output spike trains, including adaptation mechanisms
Publications and outcomes :
- Bruno Cessac and Adrian Palacios. Spike train statistics from empirical facts to theory: the case of the retina, in “Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor”, F. CAZALS, P. KORNPROBST (editors), Lectures Notes in Mathematical and Computational Biology (LNMCB), Springer-Verlag, 2013.
- Cessac, B. (2011) Statistics of spike trains in conductance-based neural networks: Rigorous results, The Journal of Mathematical Neuroscience 2011, 1:8 (25 August 2011).
- J.C. Vasquez, A. Palacios, O. Marre, M.J. Berry II, B. Cessac, Gibbs distribution analysis of temporal correlation structure on multicell spike trains from retina ganglion cells, J. Physiol. Paris, , Volume 106, Issues 3–4, May–August 2012, Pages 120–127,
Event Neural Assembly Simulation (Enas) C++ open-source middleware (interoperationable with Matlab, Java, Python, with Gtk GUI) providing :
– Statistical methods and numerical tools to analyse and simulate the statistics of spike trains obtained from retina MEA recordings.
– Variational methods and estimation tools to estimate and optimize the parameters of a neural assembly.
