M. Ernst and M. Banks, Humans integrate visual and haptic information in a statistically optimal fashion, Nature, vol.415, issue.6870, pp.429-433, 2002.
DOI : 10.1038/415429a

K. Kording and D. Wolpert, Bayesian integration in sensorimotor learning, Nature, vol.427, issue.6971, pp.244-247, 2004.
DOI : 10.1038/nature02169

D. Knill and W. Richards, Perception as Bayesian inference, 1996.
DOI : 10.1017/CBO9780511984037

E. Todorov and M. Jordan, Optimal feedback control as a theory of motor coordination, Nature Neuroscience, vol.5, issue.11, pp.1226-1235, 2002.
DOI : 10.1038/nn963

R. Zemel, P. Dayan, and A. Pouget, Probabilistic Interpretation of Population Codes, Neural Computation, vol.76, issue.4, pp.403-430, 1998.
DOI : 10.1038/370140a0

W. Ma, J. Beck, P. Latham, and A. Pouget, Bayesian inference with probabilistic population codes, Nature Neuroscience, vol.9, issue.11, pp.1432-1438, 2006.
DOI : 10.1038/nn1691

J. Beck, W. Ma, R. Kiani, T. Hanks, and A. Churchland, Probabilistic Population Codes for Bayesian Decision Making, Neuron, vol.60, issue.6, pp.1142-1152, 2008.
DOI : 10.1016/j.neuron.2008.09.021

S. Deneve, Bayesian Spiking Neurons I: Inference, Neural Computation, vol.22, issue.7, pp.91-117, 2008.
DOI : 10.1038/370140a0

R. Rao, Bayesian Computation in Recurrent Neural Circuits, Neural Computation, vol.16, issue.22, pp.1-38, 2004.
DOI : 10.1162/089976698300017818

C. Eliasmith and C. Anderson, Neural engineering: Computation, representation , and dynamics in neurobiological systems, 2003.

H. Barlow, Summation and inhibition in the frog's retina, The Journal of Physiology, vol.119, issue.1, pp.69-88, 1953.
DOI : 10.1113/jphysiol.1953.sp004829

A. Georgopoulos, J. Kalaska, R. Caminiti, and J. Massey, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex, J Neurosci, vol.2, pp.1527-1537, 1982.

M. Shadlen and W. Newsome, Noise, neural codes and cortical organization, Current Opinion in Neurobiology, vol.4, issue.4, pp.569-579, 1994.
DOI : 10.1016/0959-4388(94)90059-0

D. Tolhurst, J. Movshon, and A. Dean, The statistical reliability of signals in single neurons in cat and monkey visual cortex, Vision Research, vol.23, issue.8, pp.775-785, 1983.
DOI : 10.1016/0042-6989(83)90200-6

M. Sahani and P. Dayan, Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity, Neural Computation, vol.14, issue.8, pp.2255-2279, 2003.
DOI : 10.1162/089976699300016809

M. Jazayeri and J. Movshon, Optimal representation of sensory information by neural populations, Nature Neuroscience, vol.86, issue.5, pp.690-696, 2006.
DOI : 10.1038/nn1691

J. Beck and A. Pouget, Exact Inferences in a Neural Implementation of a Hidden Markov Model, Neural Computation, vol.86, issue.4, pp.1344-1361, 2007.
DOI : 10.1162/089976603322362356

R. Natarajan, Q. Huys, P. Dayan, and R. Zemel, Encoding and Decoding Spikes for Dynamic Stimuli, Neural Computation, vol.18, issue.18, pp.2325-2360, 2008.
DOI : 10.1162/089976699300016809

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1156

H. Seung and H. Sompolinsky, Simple models for reading neuronal population codes., Proceedings of the National Academy of Sciences, vol.90, issue.22, pp.10749-10753, 1993.
DOI : 10.1073/pnas.90.22.10749

A. Pouget, P. Dayan, and R. Zemel, Information processing with population codes, Nature Reviews Neuroscience, vol.1, issue.2, pp.125-132, 2000.
DOI : 10.1038/35039062

G. Hinton and A. Brown, Spiking boltzmann machines, Advances in Neural Information Processing Systems 12, pp.122-128, 1999.

S. Wu, D. Chen, M. Niranjan, and S. Ichi-amari, Sequential Bayesian Decoding with a Population of Neurons, Neural Computation, vol.79, issue.5, pp.993-1012, 2003.
DOI : 10.1162/089976698300017818

Q. Huys, R. Zemel, R. Natarajan, and P. Dayan, Fast Population Coding, Neural Computation, vol.79, issue.14, pp.404-441, 2007.
DOI : 10.1162/089976699300016809

S. Gerwinn, J. Macke, and M. Bethge, Bayesian population decoding of spiking neurons, Frontiers in Computational Neuroscience, vol.3, p.21, 2009.
DOI : 10.3389/neuro.10.021.2009

S. Deneve, P. Latham, and A. Pouget, Reading population codes: a neural implementation of ideal observers, Nat Neurosci, vol.2, pp.740-745, 1999.

D. Rinberg, A. Koulakov, and A. Gelperin, Speed-Accuracy Tradeoff in Olfaction, Neuron, vol.51, issue.3, pp.351-358, 2006.
DOI : 10.1016/j.neuron.2006.07.013

M. Shadlen and W. Newsome, Neural basis of a perceptual decision in the parietal cortex (area lip) of the rhesus monkey, J Neurophysiol, vol.86, pp.1916-1936, 2001.

S. Funahashi, C. Bruce, and P. Goldman-rakic, Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex, J Neurophysiol, vol.61, pp.331-349, 1989.

A. Compte, C. Constantinidis, J. Tegner, S. Raghavachari, and M. Chafee, Temporally Irregular Mnemonic Persistent Activity in Prefrontal Neurons of Monkeys During a Delayed Response Task, Journal of Neurophysiology, vol.90, issue.5, pp.3441-3454, 2003.
DOI : 10.1152/jn.00949.2002

A. Compte, N. Brunel, P. Goldman-rakic, and X. Wang, Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model, Cerebral Cortex, vol.10, issue.9, pp.910-923, 2000.
DOI : 10.1093/cercor/10.9.910

R. Ben-yishai, R. Bar-or, and H. Sompolinsky, Theory of orientation tuning in visual cortex., Proceedings of the National Academy of Sciences, vol.92, issue.9, pp.3844-3848, 1995.
DOI : 10.1073/pnas.92.9.3844

P. Poirazi, T. Brannon, and B. Mel, Pyramidal Neuron as Two-Layer Neural Network, Neuron, vol.37, issue.6, pp.989-999, 2003.
DOI : 10.1016/S0896-6273(03)00149-1

R. Romo, C. Brody, A. Hernndez, and L. Lemus, Neuronal correlates of parametric working memory in the prefrontal cortex, Nature, vol.399, issue.6735, pp.470-473, 1999.
DOI : 10.1038/20939

M. London, A. Roth, L. Beeren, M. Husser, and P. Latham, Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex, Nature, vol.83, issue.7302, pp.123-127, 2010.
DOI : 10.1038/nature09086

C. Van-vreeswijk and H. Sompolinsky, Chaotic Balanced State in a Model of Cortical Circuits, Neural Computation, vol.13, issue.6, pp.1321-1371, 1998.
DOI : 10.1016/S0006-3495(72)86068-5

H. Sompolinsky, A. Crisanti, and H. Sommers, Chaos in Random Neural Networks, Physical Review Letters, vol.61, issue.3, pp.259-262, 1988.
DOI : 10.1103/PhysRevLett.61.259

A. Faisal, L. Selen, and D. Wolpert, Noise in the nervous system, Nature Reviews Neuroscience, vol.81, issue.4, pp.292-303, 2008.
DOI : 10.1126/science.1089662

M. Steriade, I. Timofeev, and F. Grenier, Natural waking and sleep states: a view from inside neocortical neurons, J Neurophysiol, vol.85, pp.1969-1985, 2001.

Y. Shu, A. Hasenstaub, M. Badoual, T. Bal, and D. Mccormick, Barrages of synaptic activity control the gain and sensitivity of cortical neurons, J Neurosci, vol.23, pp.10388-10401, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00294475

H. Seung, D. Lee, B. Reis, and D. Tank, Stability of the Memory of Eye Position in a Recurrent Network of Conductance-Based Model Neurons, Neuron, vol.26, issue.1, pp.259-271, 2000.
DOI : 10.1016/S0896-6273(00)81155-1

A. Koulakov, S. Raghavachari, A. Kepecs, and J. Lisman, Model for a robust neural integrator, Nature Neuroscience, vol.5, issue.8, pp.775-782, 2002.
DOI : 10.1038/nn893

C. Ploner, B. Gaymard, S. Rivaud, Y. Agid, and C. Pierrot-deseilligny, Temporal limits of spatial working memory in humans, European Journal of Neuroscience, vol.17, issue.2, pp.794-797, 1998.
DOI : 10.1016/0042-6989(94)90259-3

X. Wang, Synaptic basis of cortical persistent activity: the importance of nmda receptors to working memory, J Neurosci, vol.19, pp.9587-9603, 1999.

E. Simoncelli, Optimal estimation in sensory systems, The Cognitive Neurosciences, pp.525-535, 2009.

S. Tanaka, J. Ribot, K. Imamura, and T. Tani, Orientation-restricted continuous visual exposure induces marked reorganization of orientation maps in early life, NeuroImage, vol.30, issue.2, pp.462-477, 2006.
DOI : 10.1016/j.neuroimage.2005.09.056

F. Ohl and H. Scheich, Learning-induced plasticity in animal and human auditory cortex, Current Opinion in Neurobiology, vol.15, issue.4, pp.470-477, 2005.
DOI : 10.1016/j.conb.2005.07.002

D. Feldman and M. Brecht, Map Plasticity in Somatosensory Cortex, Science, vol.310, issue.5749, pp.810-815, 2005.
DOI : 10.1126/science.1115807

J. Pillow, L. Paninski, V. Uzzell, E. Simoncelli, and E. Chichilnisky, Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model, Journal of Neuroscience, vol.25, issue.47, pp.11003-11013, 2005.
DOI : 10.1523/JNEUROSCI.3305-05.2005

T. Lochmann and S. Deneve, Information transmission with spiking Bayesian neurons, New Journal of Physics, vol.10, issue.5, p.55019, 2008.
DOI : 10.1088/1367-2630/10/5/055019

Z. Mainen and T. Sejnowski, Reliability of spike timing in neocortical neurons, Science, vol.268, issue.5216, pp.1503-1506, 1995.
DOI : 10.1126/science.7770778

P. Reinagel and R. Reid, Temporal coding of visual information in the thalamus, J Neurosci, vol.20, pp.5392-5400, 2000.

A. Banerjee, P. Seris, and A. Pouget, Dynamical Constraints on Using Precise Spike Timing to Compute in Recurrent Cortical Networks, Neural Computation, vol.76, issue.8, pp.974-993, 2008.
DOI : 10.1162/089976698300017214

R. Rao, Hierarchical bayesian inference in networks of spiking neurons, Advances in Neural Information Processing Systems 17, pp.1113-1120, 2005.

S. Denve, J. Duhamel, and A. Pouget, Optimal Sensorimotor Integration in Recurrent Cortical Networks: A Neural Implementation of Kalman Filters, Journal of Neuroscience, vol.27, issue.21, pp.5744-5756, 2007.
DOI : 10.1523/JNEUROSCI.3985-06.2007

N. Brunel and J. Nadal, Mutual Information, Fisher Information, and Population Coding, Neural Computation, vol.79, issue.7, pp.1731-1757, 1998.
DOI : 10.1007/BF00188924

URL : https://hal.archives-ouvertes.fr/hal-00143781