Denève, Sophie (PhD)

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

phone +33-144322635
fax  +33-144322935

Research Interests

Dealing with uncertainties is necessary to the survival of any living organism. Recent years have seen the growing use of models formalizing sensory perception, motor control or behavioral strategies as probabilistic inference tasks. Excitable neural structures face similar problems than behaving organisms: they receive noisy and ambiguous inputs, must accumulate evidence over time, combine unreliable cues, and compete with other neurons representing alternative interpretations of the sensory input. In my group, we apply such normative models, particularly Bayesian networks, in order to further our understanding of the function and dynamics of biological neural networks. The originality of this research is that these models are applied at the level of microscopic structures such as single synapse, neurons or microcircuits, whose computations are strongly constrained by neural biophysics and dynamics. In particular, single spikes are our elementary unit of coding and meaning.

Current Projects

Neural networks implementing hierarchical probabilistic models
Decision making with unknown sensory reliability
Are single neurons Bayesian integrators?
Computing in spiking networks: Rate coding is an illusion
Neural basis of optimal motor control
Do humans use Bayesian filtering to estimate the position of their motor effectors?
Computational Neuroscience of hallucinations


Lochmann, T., Ernst, U.A., and Denève, S., Perceptual inference predicts contextual modulations of sensory responses., Journal of Neuroscience, 32(12), 4179-95 (2012).

Deneve, S., Making decisions with unknown sensory reliability., Frontiers in Neuroscience, 6:75, doi: 10.3389/fnins.2012.00075 (2012).

Jardri, R. and Deneve, S., Computational models of hallucinations., The Neuroscience of Hallucinations, (2012).

Boerlin, M. and Denève, S., Spike-Based Population Coding and Working Memory, PLoS Comput. Biol., 7(2), (2011).

Lochmann, T. and Deneve, S., Neural processing as causal inference., Current Opinion in Neurobiology, 21(5), 774-81 (2011).

Morel, P., Deneve, S., and Baraduc, P., Optimal and suboptimal use of postsaccadic vision in sequences of saccades., Journal of Neuroscience, 31(27), 10039-49 (2011).

Wardak, C., Deneve, S., and Hamed, S.B., Focused visual attention distorts distance perception away from the attentional locus., Neuropsychologia, 49(3), 535-45 (2011).

Lochmann, T. and Deneve, S., Optimal cue combination predict contextual effects on sensory neural responses., Sensory Cue Integration, (2011).

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

Deneve, S., Bayesian approach to decision making., Handbook of reward and Decision making, (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, 20(7), 1706-16 (2008).

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

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, Nat. Neurosci., 8(7), 941-9 (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).

© Group for Neural Theory - October 20, 2011