Publications

Cristia, A., Seidl, A. & Gerken, L. (2021). Learning classes of sounds in infancy. , Vol. 17: In University of Pennsylvania Working Papers in Linguistics, 9.

Ponsot, E. & Neri, P. (2020). Inter-individual differences in spectrotemporal modulation processing despite similar audiograms : could they be used to assess supra-threshold hearing distortions? In e-Forum Acusticum 2020. doi:10.48465/fa.2020.0808

Titeux, H., Riad, R., Cao, X., Hamilakis, N., Madden, K., Cristia, A., Bachoud-Levi, A. & Dupoux, E. (2020). Seshat: A tool for managing and verifying annotation campaigns of audio data. In LREC - 2th Language Resources and Evaluation Conference, Marseille, France.

García, P., Villalba, J., Bredin, H., Du, J., Castan, D., Cristia, A., Bullock, L., Guo, L., Okabe, K., Nidadavolu, P. , Kataria, S., Chen, S., Galmant, L., Lavechin, M., Sun, L., Gill, M., Ben-Yair, B., Abdoli, S., Wang, X., Bouaziz, W., Titeux, H., Dupoux, E., Lee, K. & Dehak, N. (2019). Speaker detection in the wild: Lessons learned from JSALT 2019. In ODYSSEY 2020.

Aucouturier, J., Liuni, M. & Ponsot, E. (2017). Not so scary anymore : Screaming voices embedded in harmonic contexts are more positive and less arousing. In ESCOM.

Larsen, E., Dupoux, E. & Cristia, A. (2017). Relating unsupervised word segmentation to reported vocabulary acquisition. In INTERSPEECH-2017, 2198-2202. doi:10.21437/Interspeech.2017-937

Ludusan, B., Mazuka, R., Bernard, M., Cristia, A. & Dupoux, E. (2017). The Role of Prosody and Speech Register in Word Segmentation: A Computational Modelling Perspective. , Vol. 2: In ACL 2017, 178-183. doi:10.18653/v1/P17-2028

Ponsot, E., Déjardin, H. & Roncière, E. (2016). Controlling Programme Loudness in Individualized Binaural Rendering of Multi-Channel Audio Contents. , Vol. 140: In Audio Engineering Society .

Michon, E., Dupoux, E. & Cristia, A. (2015). Salient dimensions in implicit phonotactic learning. In INTERSPEECH-2015, 2665-2669.

Hicheur, H., Kadone, H., Grèzes, J. & Berthoz, A. (2013). Perception of emotional gaits using avatar animation of real and artificially synthesized gaits. In 5th Biannual Conference of the Humaine-Association on Affective Computing and Intelligent Interaction (ACII), Geneva, Switzerland, IEEE, 460-466. doi:10.1109/ACII.2013.82

Caze, R., Humphries, M. & Gutkin, B. (2012). Spiking and saturating dendrites differentially expand single neuron computation capacity. , Vol. 13: In Twenty First Annual Computational Neuroscience Meeting: CNS*2012, Decatur, GA, USA.

Mamassian, P. & Vemuri, B. (1993). ISOPHOTES ON A SMOOTH SURFACE RELATED TO SCENE GEOMETRY. , Vol. 2031: In Geometric Methods in Computer Vision II, 124-133. doi:10.1117/12.146619

Chevallier, C. (2021). Vivre dans un environnement risqué : quels impacts pour la psychologie ? In Claudia Senik (Eds.), Sociétés en danger (Odile Jacob ed., pp. 197 à 208). Paris: La Découverte. doi:10.3917/dec.senik.2021.01.0197

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2021). Learning and Cognition in Financial Markets: A Paradigm Shift for Agent-Based Models. Advances in Intelligent Systems and Computing (Vol. 1252, pp. 241-255). doi:10.1007/978-3-030-55190-2_19

Chevallier, C. (2019). Theory of mind and autism: Revisiting Baron-Cohen et al.’s Sally-Anne study. In A. Slater and P. Quinn (Eds.), Developmental Psychology: Revisiting the Classic Studies 2nd edition (pp. 148-163).Sage

Dumont, G., Maex, R. & Gutkin, B. (2018). Dopaminergic Neurons in the Ventral Tegmental Area and Their Dysregulation in Nicotine Addiction. In Alan Anticevic and John D. Murray (Eds.), Computational Psychiatry: Mathematical Modeling of Mental Illness (pp. 47-84). doi:10.1016/B978-0-12-809825-7.00003-1

Dezecache, G., Eskenazi, T. & Grèzes, J. (2016). Emotional Convergence: A Case of Contagion? In Sukhvinder D. Obhi & Emily S. Cross (Eds.), Shared Representations: Sensorimotor Foundations of Social Life (Cambridge University Press ed., pp. 417).

Gutkin, B. (2015). Theta-neurons. In Springer Verlag (Eds.), Encyclopedia of Comptutational Neuroscience (pp. 1034-1042).

Kuznetsov, A. & Gutkin, B. (2015). Dopaminergic cell Models. The Encyclopedia of Computational Neuroscience (pp. 2958-2965).

Remme, M., Lengyel, M. & Gutkin, B. (2015). Trade-off between dendritic democracy and independence in neurons with intrinsic subthreshold membrane potential oscillatio. In Remme et al (eds) (Eds.), Dendritic ComputationSpringer

Grèzes, J. & Dezecache, G. (2014). Bases cérébrales et cognitives de la communication émotionnelle. In M. Botbol (Eds.), L'empathie au carrefour des sciences et de la cliniqueJohn Libbey Eurotext

Remme, M., Lengyel, M. & Gutkin, B. (2014). Phase Response Methods in Dendritic Dynamics. In Schultheiss et al (eds) (Eds.), Phase Response Cruves in NeuroscienceSpringer

Caze, R., Humphries, M. & Gutkin, B. (2013). Dendrites enhance both single neuron and network computation. In Remme et al (eds) (Eds.), Dendritic ComputationSpringer

Graupner, M. & Gutkin, B. (2012). Dynamical Approaches to understanding cholinergic control of nicotine action pathways in the dopaminergic reward circuits. Computational Neuroscience of Drug Addiction (Springer ed.).Ahmed and Gutkin (eds.)

Grèzes, J. & Dezecache, G. (2012). Communication émotionnelle: mécanismes cognitifs et cérébraux. In P. Allain, G. Aubin & D. Le Gal (Eds.), Cognition Sociale et NeuropsychologieSolal

Mamassian, P., Landy, M., Maloney, L., Rao, R., Olshausen, B. & Lewicki, M. (2002). Bayesian modelling of visual perception. In R. Rao, B. Olshausen & M. Lewicki (Eds.), Probabilistic Models of the Brain: Perception and Neural Function (pp. 13-36). Cambridge, MA: MIT Press