A. Renart and C. K. Machens, Variability in neural activity and behavior, Current Opinion in Neurobiology, vol.25, pp.211-220, 2014.

V. Mountcastle, M. A. Steinmetz, and R. Romo, Frequency discrimination in the sense of flutter: psychophysical measurements correlated with postcentral events in behaving monkeys, Journal of Neuroscience, vol.10, p.2118947, 1990.

K. H. Britten, M. N. Shadlen, W. T. Newsome, and J. A. Movshon, The analysis of visual motion: a comparison of neuronal and psychophysical performance, Journal of Neuroscience, vol.12, p.1464765, 1992.

G. Werner and V. Mountcastle, Neural activity in mechanoreceptive cutaneous afferents: Stimulus-response relations, weber functions, and information transmission, Journal of Neurophysiology, vol.28, p.14283062, 1965.

W. Talbot, D. Kornhuber, H. Mountcastle, and V. , The sense of flutter-vibration: comparison of the human capacity with response patterns of mechanoreceptive afferents from the monkey hand, Journal of Neurophysiology, vol.31, p.4972033, 1968.

R. Romo and E. Salinas, Flutter discrimination: neural codes, perception, memory and decision making, Nature Reviews Neuroscience, vol.4, pp.203-221, 2003.

J. I. Gold and M. N. Shadlen, The neural basis of decision making, Annual Review of Neuroscience, vol.30, p.17600525, 2007.

M. N. Shadlen and W. T. Newsome, The variable discharge of cortical neurons: implications for connectivity, computation, and information coding, Journal of Neuroscience, vol.18, p.9570816, 1998.

L. F. Abbott and P. Dayan, The effect of correlated variability on the accuracy of a population code, Neural computation, vol.11, pp.91-101, 1999.

B. B. Averbeck, P. E. Latham, and A. Pouget, Neural correlations, population coding and computation, Nature Reviews Neuroscience, vol.7, pp.358-66, 2006.

T. Uka and G. C. Deangelis, Contribution of middle temporal area to coarse depth discrimination: comparison of neuronal and psychophysical sensitivity, Journal of Neuroscience, vol.23, p.12716961, 2003.

M. R. Cohen and W. T. Newsome, Estimates of the contribution of single neurons to perception depend on timescale and noise correlation, Journal of Neuroscience, vol.29, p.19458234, 2009.

N. Price and R. T. Born, Timescales of sensory-and decision-related activity in the middle temporal and medial superior temporal areas, Journal of Neuroscience, vol.30, p.20962225, 2010.

D. Green and J. Swets, Signal detection theory and psychophysics, vol.1974, 1966.

K. H. Britten, W. T. Newsome, M. N. Shadlen, S. Celebrini, and A. J. Movshon, A relationship between behavioral choice and the visual response of neurons in macaque MT, Visual Neuroscience, vol.13, pp.87-100, 1996.

V. De-lafuente and R. Romo, Neural correlate of subjective sensory experience gradually builds up across cortical areas, Proceedings of the National Academy of Sciences of the United States of America, vol.103, pp.14266-71, 2006.

M. N. Shadlen, K. H. Britten, W. T. Newsome, and A. J. Movshon, A computational analysis of the relationship between neuronal and behavioral responses to visual motion, Journal of Neuroscience, vol.76, pp.1486-1510, 1996.

H. Nienborg and B. G. Cumming, Correlations between the activity of sensory neurons and behavior: how much do they tell us about a neuron's causality?, Current opinion in neurobiology, vol.20, p.20545019, 2010.

R. M. Haefner, S. Gerwinn, J. H. Macke, and M. Bethge, Inferring decoding strategies from choice probabilities in the presence of correlated variability, Nature Neuroscience, vol.16, pp.235-242, 2013.

H. Nienborg and B. G. Cumming, Decision-related activity in sensory neurons reflects more than a neuron's causal effect, Nature, vol.459, pp.89-92, 2009.

A. Hernández, A. Zainos, and R. Romo, Neuronal correlates of sensory discrimination in the somatosensory cortex, Proceedings of the National Academy of Sciences of the United States of America, vol.97, pp.6191-6197, 2000.

A. M. Aertsen, G. L. Gerstein, M. K. Habib, and G. Palm, Dynamics of neuronal firing correlation: modulation of "effective connectivity, Journal of neurophysiology, vol.61, p.2723733, 1989.

R. Luna, A. Hernández, C. D. Brody, and R. Romo, Neural codes for perceptual discrimination in primary somatosensory cortex, Nature Neuroscience, vol.8, pp.1210-1219, 2005.

M. B. Ahrens, M. B. Orger, D. N. Robson, J. M. Li, and P. J. Keller, Whole-brain functional imaging at cellular resolution using light-sheet microscopy, Nature methods, vol.10, p.23524393, 2013.

T. Panier, S. A. Romano, R. Olive, T. Pietri, and G. Sumbre, Fast functional imaging of multiple brain regions in intact zebrafish larvae using selective plane illumination microscopy, Frontiers in neural circuits, vol.7, p.23576959, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00904468

R. Portugues, C. E. Feierstein, F. Engert, and M. B. Orger, Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior, Neuron, vol.81, pp.1328-1343, 2014.

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning, 2009.

A. Wohrer, M. D. Humphries, and C. K. Machens, Population-wide distributions of neural activity during perceptual decision-making, Progress in Neurobiology, vol.103, p.23123501, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01904763

A. Wohrer, R. Romo, and C. K. Machens, Linear readout from a neural population with partial correlation data, Advances in Neural Information Processing, vol.23, pp.2469-2477, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01904681

S. Turaga, L. Buesing, A. M. Packer, H. Dalgleish, and N. Pettit, Inferring neural population dynamics from multiple partial recordings of the same neural circuit, Advances in Neural Information Processing Systems, pp.539-547, 2013.

M. Boerlin, C. K. Machens, and S. Denève, Predictive coding of dynamical variables in balanced spiking networks, PLoS computational biology, vol.9, p.24244113, 2013.

M. Boerlin and S. Denève, Spike-based population coding and working memory, PLoS computational biology, vol.7, p.21379319, 2011.
URL : https://hal.archives-ouvertes.fr/inserm-00704812

M. Schaub and S. Schultz, The ising decoder: reading out the activity of large neural ensembles, Journal of Computational Neuroscience, vol.32, pp.101-118, 2012.

E. P. Cook and J. Maunsell, Dynamics of neuronal responses in macaque mt and vip during motion detection, Nature neuroscience, vol.5, pp.985-994, 2002.

T. R. Stanford, S. Shankar, D. P. Massoglia, M. G. Costello, and E. Salinas, Perceptual decision making in less than 30 milliseconds, Nature Neuroscience, vol.13, pp.379-385, 2010.

P. Ashourian and Y. Loewenstein, Bayesian inference underlies the contraction bias in delayed comparison tasks, PloS one, vol.6, p.21589867, 2011.

K. Miura, Z. F. Mainen, and N. Uchida, Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity, Neuron, vol.74, pp.1087-1098, 2012.

D. Daley, V. , and D. , An introduction to the theory of point processes, vol.1, 2007.

D. Goodman and R. Brette, Brian: a simulator for spiking neural networks in python, Frontiers in neuroinformatics 2, 2008.

S. Raudys and R. Duin, Expected classification error of the fisher linear classifier with pseudoinverse covariance matrix, Pattern Recognition Letters, vol.19, pp.16-22, 1998.

D. C. Hoyle, Accuracy of pseudo-inverse covariance learning-a random matrix theory analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.33, pp.1470-1481, 2011.

C. M. Bishop, Pattern recognition and machine learning, 2006.