Theoretical Neuroscience, 2001. ,
Spike-timing-dependent plasticity for neurons with recurrent connections, Biological Cybernetics, vol.3, issue.7, pp.533-546, 2007. ,
DOI : 10.1007/s00422-007-0148-2
A discrete time neural network model with spiking neurons, Journal of Mathematical Biology, vol.18, issue.26, pp.311-345, 2008. ,
DOI : 10.1007/s00285-007-0117-3
URL : https://hal.archives-ouvertes.fr/inria-00530115
Statistics of spike trains in conductance-based neural networks: Rigorous results, The Journal of Mathematical Neuroscience, vol.1, issue.1, pp.1-42, 2011. ,
DOI : 10.1038/nature05534
URL : https://hal.archives-ouvertes.fr/hal-00640501
Spike train statistics and Gibbs distributions, Journal of Physiology-Paris, vol.107, issue.5, pp.360-368, 2013. ,
DOI : 10.1016/j.jphysparis.2013.03.001
URL : https://hal.archives-ouvertes.fr/hal-00850155
Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina, In Modeling in Computational Biology and Biomedicine, pp.261-302, 2013. ,
DOI : 10.1007/978-3-642-31208-3_8
URL : https://hal.archives-ouvertes.fr/hal-00640507
Parametric estimation of spike train statistics by Gibbs distributions: an application to bio-inspired and experimental data, Cinquième conférence plénière française de Neurosciences Computationnelles, pp.1-5, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00553441
On Dynamics of Integrate-and-Fire Neural Networks with Conductance Based Synapses, Frontiers in Computational Neuroscience, vol.2, issue.2, 2008. ,
DOI : 10.3389/neuro.10.002.2008
Pressure and equilibrium states in ergodic theory, 2011. ,
Aspects of ergodic, qualitative and statistical theory of motion, 2004. ,
DOI : 10.1007/978-3-662-05853-4
Funzioni speciali. Unione tipografico-editrice torinese, 1973. ,
Learning reward timing in cortex through reward dependent expression of synaptic plasticity, Proceedings of the National Academy of Sciences, vol.106, issue.16, pp.6826-6831, 2009. ,
DOI : 10.1073/pnas.0901835106
Integrate-and-fire neurons and networks. The handbook of brain theory and neural networks, pp.1-12, 1998. ,
Spiking Neuron Models single neurons, populations, plasticity, 2002. ,
Neural codes: Firing rates and beyond, Proceedings of the National Academy of Sciences of the United States of America, pp.12740-12741, 1997. ,
DOI : 10.1073/pnas.94.24.12740
STDP in recurrent neuronal networks, Frontiers in computational neurosciencecomputational neuroscience, pp.1-15, 2010. ,
DOI : 10.3389/fncom.2010.00023
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV, Biological Cybernetics, vol.19, issue.3, pp.427-444, 2009. ,
DOI : 10.1007/s00422-009-0346-1
STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains, PLoS Computational Biology, vol.2, issue.10, p.1002231, 2011. ,
DOI : 10.1371/journal.pcbi.1002231.s001
Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity, The Journal of neuroscience : the official journal of the Society for Neuroscience, vol.23, issue.9, pp.3697-3714, 2003. ,
Poisson model of spike generation. Handout, pp.1-13, 2000. ,
Spike-timing Dynamics of Neuronal Groups, Cerebral Cortex, vol.14, issue.8, pp.933-944, 2004. ,
DOI : 10.1093/cercor/bhh053
A history of spiketiming-dependent plasticity, Frontiers in computational neuroscience, vol.3, pp.1-24, 2011. ,
Cell Populations of the Retina: The Proctor Lecture, Investigative Opthalmology & Visual Science, vol.52, issue.7, pp.4581-4591, 2011. ,
DOI : 10.1167/iovs.10-7083
Learning and Coding in??Neural??Networks, Principles of Neural Coding, chapter 26, pp.513-526, 2012. ,
DOI : 10.1201/b14756-30
SPAN: SPIKE PATTERN ASSOCIATION NEURON FOR LEARNING SPATIO-TEMPORAL SPIKE PATTERNS, International Journal of Neural Systems, vol.22, issue.04, pp.1-16, 2012. ,
DOI : 10.1142/S0129065712500128
STDP enables spiking neurons to detect hidden causes of their inputs, Advances in Neural Information Processing Systems, pp.1357-1365, 2010. ,
Zeta functions and the periodic orbit structure of hyperbolic dynamics. Asterisque, 1990. ,
On goodness of fit tests for models of neuronal spike trains considered as counting processes, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00416793
Interspike intervals, receptive fields, and information encoding in primary visual cortex, The Journal of neuroscience, vol.20, issue.5, pp.1964-1974, 2000. ,
Generation of correlated spike trains, Neural computation, vol.21, pp.188-215, 2009. ,
Computing with spikes, architecture, properties and implementation of emerging paradigms, 2011. ,
Weak pairwise correlations imply strongly correlated network states in a neural population, Nature, vol.37, issue.7087, pp.1007-1012, 2006. ,
DOI : 10.1038/nature04701
Notes on kullback-leibler divergence and likelihood theory, Systems Neurobiology Laboratory, vol.92037, pp.1-4, 2007. ,
The structure of multineuron firing patterns in primate retina, The Journal of neuroscience, issue.32, pp.268254-8266, 2006. ,
Competitive Hebbian learning through spike-timing-dependent synaptic plasticity, Nature neuroscience, vol.3, issue.9, pp.919-926, 2000. ,
How Gibbs Distributions May Naturally Arise from Synaptic Adaptation Mechanisms. A Model-Based Argumentation, Journal of Statistical Physics, vol.136, issue.6, pp.565-602, 2009. ,
URL : https://hal.archives-ouvertes.fr/inria-00407910
Entropy-based parametric estimation of spike train statistics, 2010. ,
URL : https://hal.archives-ouvertes.fr/inria-00534847
Virtual Retina: A biological retina model and simulator, with contrast gain control, Journal of Computational Neuroscience, vol.32, issue.3, pp.219-249, 2009. ,
DOI : 10.1007/s10827-008-0108-4
URL : https://hal.archives-ouvertes.fr/inria-00160716