Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation, Journal of Physiology-Paris, vol.94, issue.5-6, pp.569-582, 2000. ,
DOI : 10.1016/S0928-4257(00)01100-1
Routes to chaos in high-dimensional dynamical systems: A qualitative numerical study, Physica D: Nonlinear Phenomena, vol.223, issue.2, pp.194-207, 2006. ,
DOI : 10.1016/j.physd.2006.09.004
Structural stability and hyperbolicity violation in high-dimensional dynamical systems, Nonlinearity, vol.19, issue.8, pp.1801-1847, 2006. ,
DOI : 10.1088/0951-7715/19/8/005
Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons, Journal of Neuroscience Methods, vol.124, issue.2, 2003. ,
DOI : 10.1016/S0165-0270(03)00006-2
Nature is the code: high temporal precision and low noise in V1, 2007. ,
Implementing the Simplex Method: The Initial Basis, ORSA Journal on Computing, vol.4, issue.3, 1992. ,
DOI : 10.1287/ijoc.4.3.267
Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity, Neural Computation, vol.20, issue.24, pp.371-403, 2007. ,
DOI : 10.1038/25665
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 spikes trains, synaptic plasticity and gibbs distributions, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00331541
To which extend is the " neural code " a metric ?, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00331567
To which extend is the " neural code " a metric ? InDeuxì eme conférence française de Neurosciences Computationnelles, 2008. ,
Introducing numerical bounds to improve event-based neural network simulation, Frontiers in neuroscience, 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00382534
On dynamics of integrate-and-fire neural networks with adaptive conductances, Frontiers in neuroscience, vol.2, issue.2, 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00338369
Relative Entropy and Identification of Gibbs Measures in Dynamical Systems, Journal of Statistical Physics, vol.90, issue.3-4, pp.3-4697, 1998. ,
DOI : 10.1023/A:1023220802597
Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization, Neural Computation, vol.395, issue.3, pp.1481-1510, 2003. ,
DOI : 10.1016/S0896-6273(01)00460-3
URL : http://ai.stanford.edu/~gal/Papers/chechik_stdp.pdf
Introduction to Linear ProgrammingApplications and Extensions, 1990. ,
Networks of integrate-and-fire neurons using Rank Order Coding B: Spike timing dependent plasticity and emergence of orientation selectivity, Neurocomputing, vol.38, issue.40, pp.539-545, 2001. ,
DOI : 10.1016/S0925-2312(01)00403-9
Conductance-Based Integrate-and-Fire Models, Neural Computation, vol.44, issue.3, pp.503-514, 1997. ,
DOI : 10.1016/S0006-3495(91)82186-5
URL : https://hal.archives-ouvertes.fr/hal-00120630
Matrix Theory, 1977. ,
Mathematical formulations of Hebbian learning, Biological Cybernetics, vol.87, issue.5-6, pp.404-415, 2002. ,
DOI : 10.1007/s00422-002-0353-y
Neurons Tune to the Earliest Spikes Through STDP, Neural Computation, vol.76, issue.4, 2004. ,
DOI : 10.1038/25665
URL : https://hal.archives-ouvertes.fr/hal-00330516
Algorithms for simultaneous sparse approximation. part i: Greedy pursuit, Signal Processing, vol.86, pp.572-588, 2006. ,
Adaptive nonlinear system identification with Echo State Networks, Advances in Neural Information Processing Systems, pp.593-600, 2002. ,
Introduction to the modern theory of dynamical systems. Kluwer, 1998. INRIA Reverse-engineering in spiking neural networks parameters: exact deterministic parameters estimation41 ,
Modeling cortical maps with feed-backs, 29th European Conference on Visual Perception, p.53, 2006. ,
Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model, J. Neurophysiol, vol.92, pp.959-976, 2004. ,
Fast Sigmoidal Networks via Spiking Neurons, Neural Computation, vol.47, issue.2, pp.279-304, 1997. ,
DOI : 10.1038/367069a0
On the relevance of time in neural computation and learning, LNAI 1316, pp.157-178364, 2001. ,
DOI : 10.1016/S0304-3975(00)00137-7
Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding, Network: Computation in Neural Systems, vol.8, issue.4, pp.355-372, 1997. ,
DOI : 10.1088/0954-898X_8_4_002
Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations, Neural Computation, vol.7, issue.11, pp.2531-2560, 2002. ,
DOI : 10.1038/35009102
Delay learning and polychronization for reservoir computing, Neurocomputing, vol.71, issue.7-9, pp.1143-1158, 2008. ,
DOI : 10.1016/j.neucom.2007.12.027
Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy, J. Neurophysiol, vol.92, pp.959-976, 2004. ,
Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies, Neural Computation, vol.18, issue.9, pp.2146-2210, 2006. ,
DOI : 10.1103/PhysRevLett.71.1280
URL : https://hal.archives-ouvertes.fr/hal-00120630
RECURRENT NEURAL NETWORKS ARE UNIVERSAL APPROXIMATORS, International Journal of Neural Systems, vol.17, issue.04, pp.632-640, 2006. ,
DOI : 10.1142/S0129065707001111
Towards Applicable Spiking Neural Networks, 2007. ,
Stochastic Dynamics of a Finite-Size Spiking Neural Network, Neural Computation, vol.13, issue.1, pp.3262-3292, 2007. ,
DOI : 10.1162/089976698300017214
URL : https://hal.archives-ouvertes.fr/hal-00759513
Dale???s principle, Brain Research Bulletin, vol.50, issue.5-6, p.34950, 1999. ,
DOI : 10.1016/S0361-9230(99)00100-8
Reinforcement Learning: An Introduction, 1998. ,
Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution, Neural Computation, vol.20, issue.3, pp.639-671, 2007. ,
DOI : 10.1126/science.1082212
Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission, Proceedings of the National Academy of Science, pp.5239-5244, 2005. ,
DOI : 10.1073/pnas.0500495102
Algorithms for simultaneous sparse approximation. part ii: Convex relaxation. Sparse approximations in signal processing, pp.589-602, 2006. ,
Greed is Good: Algorithmic Results for Sparse Approximation, IEEE Transactions on Information Theory, vol.50, issue.10, pp.2231-2242, 2004. ,
DOI : 10.1109/TIT.2004.834793
Just relax: Convex programming methods for subset selection and sparse approximation, Texas Institute for Computational Engineering and Sciences, 2004. ,
An experimental unification of reservoir computing methods, Neural Networks, vol.20, issue.3, pp.391-403, 2007. ,
DOI : 10.1016/j.neunet.2007.04.003
Spike train metrics, Current Opinion in Neurobiology, vol.15, issue.5, pp.585-592, 2005. ,
DOI : 10.1016/j.conb.2005.08.002
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2713191
Nature and precision of temporal coding in visual cortex: a metric-space analysis, J Neurophysiol, vol.76, pp.1310-1326, 1996. ,
Implementing a multi-model estimation method, The International Journal of Computer Vision, vol.44, issue.1, 2001. ,
One step towards an abstract view of computation in spiking neural-networks, International Conf. on Cognitive and Neural Systems, 2006. ,
On the nonlearnability of a single spiking neuron, Neural Computation, vol.17, issue.12, pp.2635-2647, 2005. ,