Outcomes : Analysing

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 :

• Rodrigo Cofré, Bruno Cessac, “Hearing the Maximum Entropy Potential of a spike-generating Markov process”, submitted to Phys. Rev. Letters.

• Bruno Cessac and Rodrigo Cofré, Spike train statistics and Gibbs distributions,  J. Physiol. Paris, Volume 107, Issue 5, Pages 360-368 (November 2013). Special issue: Neural Coding and Natural Image Statistics.

• Rodrigo Cofré and Bruno Cessac Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses, Chaos, Solitons & Fractals, Volume 50, May 2013, Pages 13-31.

• Hassan Nasser, Olivier Marre, and Bruno Cessac. Spike trains analysis using gibbs distributions and monte-carlo method”, J. Stat. Mech. (2013) P03006.

• 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.

http://enas.gforge.inria.fr