Group for Neural Theory, Computational Neuroscience

Denève, Sophie (PhD)

Group for Neural Theory, DEC, ENS
ground floor, bureau 65/66
29 rue d'Ulm
75005 Paris


phone	+33-144322635
fax	+33-144322935
email

Research Interests

Theories of Bayesian inference and learning have been very successful recently in describing the behaviors of humans and animals, and particularly their perceptual and motor biases. Indeed, the tasks humans are facing in 'natural' situations (as opposite to simplistic laboratory settings) require the combination of multiple noisy and ambiguous sensory cues, as well as the use of prior knowledge, either innate or acquired from previous experiences. Such incomplete and imperfect sensory cues and prior knowledge can only provide probabilistic information (such as object structures that are more likely, or the probability of moving in a particular direction given the observed optic flow). Computer vision faces, of course, similar problems.
Our research focus on studying and modeling how the neural substrate perform these Bayesian inference tasks. The Bayesian framework has the advantage to be modular and apply to a wide range of cognitive tasks. Thus, we use different levels of analysis (from single neurons to small circuits, large populations and networks of areas) and concentrate on apparently very different neural systems (basic vision, multisensory integration, motor control) in the hope of deriving some generic principle for probabilistic neural coding and computation.

Current Projects

	Neural networks implementing hierarchical probabilistic models
	Are single neurons Bayesian integrators?
	Neural basis of optimal motor control
	Do humans use Bayesian filtering to estimate the position of their motor effectors?

Publications

Munuera, J., Morel, P., Duhamel, J., and Denève, S., Optimal sensorimotor control in eye movement sequences, Journal of Neuroscience, 29, 3026-35 (2009).

Denève, S., Bayesian Spiking Neurons I: Inference, Neural Computation, 20, 91-117 (2008).

Denève, S., Bayesian Spiking Neurons II: Learning, Neural Computation, 20, 118-145 (2008).

Mongillo, G. and Denève, S., Online Learning with Hidden Markov Models, Neural Computation, in press (2008).

Lochmann, T. and Denève, S., Information transmission with spiking Bayesian neurons, New Journal of Physics, 10, article ID: 055019 (2008).

Ernst, U., Denève, S., and Meinhardt, G., Detection of Gabor patch arrangements is explained by natural image statistics, CNS*2007 conference, submitted (2007).

Denève, S., Duhamel, J., and Pouget, A., Optimal sensorimotor integration in recurrent cortical networks: a neural implementation of Kalman filters, Journal of Neuroscience, 27, 5744-5756 (2007).

Rouger, J., Lagleyre, S., Fraysse, B., Denève, S., Deguine, O., and Barone, P., Evidence that cochlear-implanted deaf patients are better multisensory integrators., Proceedings of the National Academy of Sciences USA, 104(17), 7295-7300. (2007).

Avillac, M., Denève, S., Olivier, E., Pouget, A., and Duhamel, J.R., Reference frames for representing visual and tactile locations in parietal cortex, Nature Neuroscience, 8 (7), 941-949 (2005).

Denève, S. and Pouget, A., Bayesian multisensory integration and cross-modal spatial links, Journal of Neurophysiology (Paris), 98 (1-3), 249-258 (2004).

Denève, S. and Pouget, A., Basis functions for object-centered representations, Neuron, 37 (2), 347-359 (2003).

Latham, P., Denève, S., and Pouget, A., Optimal computations with attractor networks, Journal of Neurophysiology (Paris), 97 (4-6), 683-694 (2003).

Pouget, A., Denève, S., and Duhamel, J.R., A Computational Perspective on the Neural Basis of Multisensory Spatial Representations, Nature Review Neuroscience, 3, 741-747 (2002).

Denève, S., Latham, P.E., and Pouget, A., Efficient computation and cue integration with noisy population codes, Nature Neuroscience, 4 (8), 826-831 (2001).

Denève, S., Latham, P.E., and Pouget, A., Reading population codes: a neural implementation of ideal observers, Nature Neuroscience, 2 (8), 740-745 (1999).

Pouget, A., Denève, S., and Sejnowski, T., Frames of reference in hemineglect: a computational approach, Progress in Brain Research, 121, 81-97 (1999).

Pouget, A., Denève, S., Ducom, J., and Latham, P., Narrow versus wide tuning curves: What's best for a population code?, Neural Computation, 11(1), 85-90 (1999).

Pouget, A., Zhang, K., Denève, S., and Latham, P., Statistically efficient estimation using population coding, Neural Computation, 10(2), 373-401 (1998).