Y. Amitai, J. Gibson, M. Beirleiner, S. L. Patrick, A. M. Ho et al., The spatial dimensions of electrically coupled networks of interneurons in neocortex, J. Neurosci, vol.22, pp.4142-4152, 2002.

R. Azouz and C. M. Gray, Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo, Proceedings of the National Academy of Science, pp.8110-8115, 2000.
DOI : 10.1073/pnas.130200797

R. Brette and W. Gerstner, Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity, Journal of Neurophysiology, vol.94, issue.5, pp.3637-3642, 2005.
DOI : 10.1152/jn.00686.2005

R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman et al., Simulation of networks of spiking neurons: A review of tools and strategies, Journal of Computational Neuroscience, vol.25, issue.54, pp.349-398, 2007.
DOI : 10.1007/s10827-007-0038-6
URL : https://hal.archives-ouvertes.fr/hal-00180662

R. Brette, Exact Simulation of Integrate-and-Fire Models with Synaptic Conductances, Neural Computation, vol.9, issue.37, pp.2004-2027, 2006.
DOI : 10.1038/36335

R. Brette, Exact Simulation of Integrate-and-Fire Models with Exponential Currents, Neural Computation, vol.19, issue.10, pp.2604-2609, 2007.
DOI : 10.1523/JNEUROSCI.3508-05.2005

G. La-camera, M. Giugliano, W. Senn, and S. Fusi, The response of cortical neurons to in vivo-like input current: theory and experiment, Biological Cybernetics, vol.1, issue.20, pp.4-5279, 2008.
DOI : 10.1007/s00422-008-0272-7

G. La-camera, M. Giugliano, W. Senn, and S. Fusi, The response of cortical neurons to in vivolike input current: theory and experiment: Ii. time-varying and spatially distributed inputs, Biological Cybernetics, vol.99, pp.4-5303, 2008.

B. Cessac, 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

B. Cessac, H. Rostro-gonzalez, J. C. Vasquez, and T. Viéville, To which extend is the " neural code " a metric ?, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00331567

B. Cessac and T. Viéville, 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

C. G. Connolly, I. Marian, and R. G. Reilly, APPROACHES TO EFFICIENT SIMULATION WITH SPIKING NEURAL NETWORKS, Connectionist Models of Cognition and Perception II, pp.231-240, 2004.
DOI : 10.1142/9789812702784_0022

A. Destexhe, M. Rudolph, and D. Paré, The high-conductance state of neocortical neurons in vivo, Nature Reviews Neuroscience, vol.4, issue.9, pp.739-751, 2003.
DOI : 10.1038/nrn1198
URL : https://hal.archives-ouvertes.fr/hal-00299172

A. Destexhe, 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

M. Galarreta and S. Hestrin, Electrical synapses between Gaba-Releasing interneurons, Nature Reviews Neuroscience, vol.235, issue.6, pp.425-433, 2001.
DOI : 10.1038/35077566

W. Gerstner and W. Kistler, Spiking Neuron Models, 2002.

M. L. Hines and N. T. Carnevale, Discrete event simulation in the NEURON environment, Neurocomputing, vol.58, issue.60, pp.1117-1122, 2004.
DOI : 10.1016/j.neucom.2004.01.175

A. L. Hodgkin and A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology, vol.117, issue.4, pp.500-544, 1952.
DOI : 10.1113/jphysiol.1952.sp004764

E. Izhikevich, Simple model of spiking neurons, IEEE Transactions on Neural Networks, vol.14, issue.6, pp.1569-1572, 2003.
DOI : 10.1109/TNN.2003.820440

E. M. Izhikevich, Which Model to Use for Cortical Spiking Neurons?, IEEE Transactions on Neural Networks, vol.15, issue.5, pp.1063-1070, 2004.
DOI : 10.1109/TNN.2004.832719

C. Koch, Biophysics of Computation: Information Processing in Single Neurons, 1999.

G. Lee and N. Farhat, The double queue method: a numerical method for integrate-and-fire neuron networks, Neural Networks, vol.14, issue.6-7, pp.921-932, 2001.
DOI : 10.1016/S0893-6080(01)00034-X

T. J. Lewis and J. , Dynamics of spiking neurons connected by both inhibitory and electrical coupling, Journal of Computational Neuroscience, vol.14, issue.3, pp.283-309, 2003.
DOI : 10.1023/A:1023265027714

W. Maass, Fast Sigmoidal Networks via Spiking Neurons, Neural Computation, vol.47, issue.2, pp.279-304, 1997.
DOI : 10.1038/367069a0

W. Maass and T. Natschlager, 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

H. Markram, J. Lübke, M. Frotscher, and B. Sakmann, Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs, Science, vol.275, issue.5297, p.275, 1997.
DOI : 10.1126/science.275.5297.213

M. Mattia and P. D. Giudice, Efficient Event-Driven Simulation of Large Networks of Spiking Neurons and Dynamical Synapses, Neural Computation, vol.12, issue.10, pp.2305-2329, 2000.
DOI : 10.1038/1131

A. Morrison, C. Mehring, T. Geisel, A. D. Aerstsen, and M. Diesmann, Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing, Neural Computation, vol.18, issue.10, pp.1776-1801, 2005.
DOI : 10.1016/0166-2236(94)90121-X

A. Morrison, S. Straube, H. E. Plesser, and M. Diesmann, Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network Simulations, Neural Computation, vol.1, issue.1, pp.47-79, 2007.
DOI : 10.1023/A:1012885314187

A. Mouraud, D. Héì-ene-paugam-moisy, and . Puzenat, A distributed and multithreaded neural event-driven simulation framework, Int. Conf. on Parallel and Distributed Computing and Networks, pp.393-398, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00015137

J. Pfister and W. Gerstner, Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity, Journal of Neuroscience, vol.26, issue.38, pp.9673-9682, 2006.
DOI : 10.1523/JNEUROSCI.1425-06.2006

A. Rauch, G. L. Camera, H. Luscher, W. Senn, and S. Fusi, Neocortical Pyramidal Cells Respond as Integrate-and-Fire Neurons to In Vivo-Like Input Currents, Journal of Neurophysiology, vol.90, issue.3, pp.1598-1612, 2003.
DOI : 10.1152/jn.00293.2003

J. Reutimann, M. Guigliano, and S. Fusi, Event-Driven Simulation of Spiking Neurons with Stochastic Dynamics, Neural Computation, vol.19, issue.21, p.811830, 2003.
DOI : 10.1038/1131

O. Rochel and D. Martinez, An event-driven framework for the simulation of networks of spiking neurons, Proc. 11th European Symposium on Artificial Neural Networks, pp.295-300, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00099501

S. Rotter and M. Diesmann, Exact digital simulation of time-invariant linear systems with applications to neuronal modeling, Biological Cybernetics, vol.81, issue.5-6, pp.381-402, 1999.
DOI : 10.1007/s004220050570

M. Rudolph and A. Destexhe, 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

M. Rudolph and A. Destexhe, How much can we trust neural simulation strategies? Neurocomputing, 2007.
DOI : 10.1016/j.neucom.2006.10.138
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.9791

B. Ruf, Computing and Learning with Spiking Neurons -Theory and Simulations, 1998.

B. Schrauwen, Towards Applicable Spiking Neural Networks, 2007.

J. Touboul, Bifurcation analysis of a general class of non-linear integrate and fire neurons, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00142987

J. Touboul, Bifurcation Analysis of a General Class of Nonlinear Integrate-and-Fire Neurons, SIAM Journal on Applied Mathematics, vol.68, issue.4, pp.1045-1079, 2008.
DOI : 10.1137/070687268
URL : https://hal.archives-ouvertes.fr/inria-00142987

J. D. Victor, 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

T. Viéville, S. Chemla, and P. Kornprobst, How do high-level specifications of the brain relate to variational approaches?, Journal of Physiology-Paris, vol.101, issue.1-3, pp.118-135, 2007.
DOI : 10.1016/j.jphysparis.2007.10.010

T. Viéville and O. , One step towards an abstract view of computation in spiking neural-networks, International Conf. on Cognitive and Neural Systems, 2006.

P. Tim, L. F. Vogels, and . Abbott, Signal propagation and logic gating in networks of integrate-and-fire neurons, J. Neuroscience, vol.25, p.1078610795, 2005.

T. P. Vogels, K. Rajan, and L. F. Abbott, NEURAL NETWORK DYNAMICS, Annual Review of Neuroscience, vol.28, issue.1, p.35776, 2005.
DOI : 10.1146/annurev.neuro.28.061604.135637

C. J. Wilson, A. Weyrick, D. Terman-amd, N. E. Hallworth, and M. D. Bevan, A Model of Reverse Spike Frequency Adaptation and Repetitive Firing of Subthalamic Nucleus Neurons, Journal of Neurophysiology, vol.91, issue.5, pp.1963-1980, 2004.
DOI : 10.1152/jn.00924.2003

A. Wohrer and P. Kornprobst, Virtual Retina: A biological retina model and simulator, with contrast gain control, Journal of Computational Neuroscience, vol.32, issue.3, 2008.
DOI : 10.1007/s10827-008-0108-4
URL : https://hal.archives-ouvertes.fr/inria-00160716

G. Zheng, A. Tonnelier, and D. Martinez, Voltage-stepping schemes for the simulation of spiking neural networks, Journal of Computational Neuroscience, vol.16, issue.3, 2004.
DOI : 10.1007/s10827-008-0119-1
URL : https://hal.archives-ouvertes.fr/inria-00189386