Group for Neural Theory, LNC, DEC, ENS
second floor, GNT office
29, rue d'Ulm
Cortical neural responses are hugely variable, redundant, and robust. Does the brain relies on huge numbers of unreliable units to compensate for such "noise"? Are spikes random samples from an underlying firing rate? Our recent work, combining theory and analysis of multi-unit recordings in retina and cortex, suggests quite the contrary. Despite apparences, biological neural networks are exquisitely tuned to represent relevant stimuli as reliably and efficiently as possible, spike after spike. The signature of this efficiency is the balance between excitation and inhibition maintained at all levels of cortical neural processing. This introduces an entirely new theoretical and experimental framework to explore neural coding, plasticity and adaptation.
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. We apply such normative models, particularly Bayesian networks, in order to further our understanding of the function and dynamics of biological neural networks.
The E/I balance is equally important at a much larger scale, when we consider how different brain areas communicate. Cognition, motor action and decision making are inherently hierarchical, and this hierarchy is reflected in the brain organisation. We consider how E/I balance in a hierachical neural networks would affect perception, motor control and decision making at the macroscopic, behavioral scale. We find that it can cause circular inference, where prior beliefs are "mixed-up" and mistaken as sensory information, and vice-versa. We hypothesize that such a dis-regulation could be involved in the formation of hallucinations and delusions. We are currently testing this hypothesis by performing behavioral experiments in schizophenic patients and controls.
POSTDOCTORAL POSITIONS ARE AVAILABLE. CONTACT email@example.com
CA6b: Machine learning for neuroscience
CO6: Introduction to theoretical neuroscience
Savin C., Deneve S. Spatio-temporal Representations of Uncertainty in Spiking Neural Networks, Advances in Neural Information Processing Systems, 2024-2032 (2014)
Schwemmer M.A., Fairhall A.L., Deneve S., Shea-Brown E.T., Constructing precisely computing networks with biophysical spiking neurons. arXiv:1411.3191
Notredame C.E., Pins D., Deneve S., Jardri R., What visual illusions teach us about schizophrenia., Front Integr Neurosci. 12;8:63 (2014).
Jardri R, Deneve S., Circular inferences in schizophrenia. Brain, 136-11 (2013).
DG Barrett, S Deneve, CK Machens. Firing rate predictions in optimal balanced networks. Advances in Neural Information Processing Systems, 1538-1546 (2013).
Boerlin M, Machens CK, Deneve S., Predictive coding of dynamical variablesin balanced spiking networks. PLoS Comput Biol, 9(11) (2013).
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).