Publications

Ramus, F., Peperkamp, S., Christophe, A., Jacquemot, C., Kouider, S. & Dupoux, E. (2010). A Psycholinguistic Perspective on the Acquisition of Phonology. In C. Fougeron (Eds.), Laboratory Phonology (Vol. 10 ).De Gruyter Mouton

Graves, J., Egré, P., Pressnitzer, D. & de Gardelle, V. (2021). An implicit representation of stimulus ambiguity in pupil size. , Vol. 18: In Proceedings of the National Academy of Sciences, e2107997118. doi:10.1073/pnas.2107997118

Pressnitzer, D., Agus, T., Kang, H. , Graves, J. & Andrillon, T. (2021). Apprentissage de motifs sonores. In S. Samson, B. Tillmann, C. Jourdan, V. Brun (Eds.), Audition et Cognition Montpellier: Sauramps Medical

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

Gutkin, B. & Stiefel, K. (2014). Cholinergic Neuromodulation of Phase Response Curves. In Schultheiss et al (eds) (Eds.), Phase Response Cruves in NeuroscienceSpringer

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 .

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

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

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.)

Bachoud-Levi, A. (2017). From open to large-scale randomized cell transplantation trials in Huntington's disease: Lessons from the multicentric intracerebral grafting in Huntington's disease trial (MIG-HD) and previous pilot studies. Prog Brain Res. (Vol. 230, pp. 227-261). doi:10.1016/bs.pbr.2016.12.011

Fenelon, G. (2019). Hallucinations visuelles. In Hélène Amievia, Antoinette Prouteau, Olivier Martinaud (Eds.), Neuropsychologie en psychiatrie

Parikh, R. , Kavalerov, I. , Espy-Wilson, C. & Shamma, S. (2022). Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems. In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 536-540. doi:10.1109/ICASSP43922.2022.9747314

Riad, R., Bachoud-Levi, A., Rudzicz , F. & Dupoux, E. (2020). Identification of primary and collateral tracks in stuttered speech. , Vol. Proceedings of The 12th Language Resources and Evaluation Conference: In LREC, European Language Resources Association, 1681–1688.

Berthommier, F. & Lorenzi, C. (2021). Implications of Physiological Mechanisms of Amplitude Modulation Processing for Modeling Complex Sounds Analysis and Separation. (pp. 12). doi:10.1201/9781003064183

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

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

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

Alemi, A., Machens, C., Denève, S. & Slotine, J. (2018). Learning nonlinear dynamics in efficient, balanced spiking networks using local plasticity rules. In Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, AAAI Press.

Scharenborg, O., Besacier, L., Black, A., Hasegawa-Johnson, M., Metze, F., Neubig, G., Stuker, S., Godard, P., Muller, M., Ondel, L., Palaskar, S., Arthur, P., Ciannella, F., Du, M., Larsen, E., Merkx, D., Riad, R., Wang, L. & Dupoux, E. (2018). Linguistic Unit Discovery From Multi-Modal Inputs In Unwritten Languages: Summary Of The “Speaking Rosetta” Jsalt 2017 Workshop. In Jsalt 2017 Workshop.

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.

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

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.

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.

Shamma, S. (2020). Temporal Coherence Principle in Scene Analysis. The Senses: A Comprehensive Reference (Second Edition) (Vol. 2, pp. 777-790).Elsevier. doi:10.1016/B978-0-12-809324-5.24252-1

M Siriwardena, Y., Marion, G. & Shamma, S. (2022). The Mirrornet: Learning Audio Synthesizer Controls Inspired by Sensorimotor Interaction. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 946-950. doi:10.1109/ICASSP43922.2022.9747358

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

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