Publications

Zhang, D., Gao, M., Xu, D., Shi, W., Gutkin, B., Steffensen, S., Lukas, R. & Wu, J. (2012). Impact of prefrontal cortex in nicotine-induced excitation of ventral tegmental area dopamine neurons in anesthetized rats. Journal of Neuroscience, 32(36), 12366-12375. doi:10.1523/JNEUROSCI.5411-11.2012

Zeldenrust, F., De Knecht, S., Wadman, W., Denève, S. & Gutkin, B. (2017). Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series. Frontiers in computational neuroscience, 11, 49. doi:10.3389/fncom.2017.00049

Zeghidour, N., Usunier, N., Synnaeve, G., Collobert, R. & Dupoux, E. (2019). End-to-End Speech Recognition from the raw waveform. In Interspeech-2018.

Zeghidour, N., Usunier, N., Synnaeve, G., Collobert, R. & Dupoux, E. (2018). End-to-End Speech Recognition From the Raw Waveform. In Interspeech 2018. doi:10.21437/Interspeech.2018-2414

Zeghidour, N., Usunier, N., Kokkinos, I., Schatz, T., Synnaeve, G. & Dupoux, E. (2018). Learning Filterbanks from Raw Speech for Phoneme Recognition. In ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing.

Zeghidour, N., Synnaeve, G., Usunier, N. & Dupoux, E. (2016). Joint Learning of Speaker and Phonetic Similarities with Siamese Networks. In INTERSPEECH-2016, 1295-1299.

Zeghidour, N., Synnaeve, G., Versteegh, M. & Dupoux, E. (2016 ). A Deep Scattering Spectrum - Deep Siamese Network Pipeline For Unsupervised Acoustic Modeling. In ICASSP-2016, 4965-4969.

Zamani, A. , Novikov, N. & Gutkin, B. (2020). Concomitance of inverse stochastic resonance and stochastic resonance in a minimal bistable spiking neural circuit. Communications in Nonlinear Science and Numerical Simulation, 82, 105024. doi:10.1016/j.cnsns.2019.105024

Zakharov, D., Dogonasheva, O. & Gutkin, B. (2020). Role of Pyramidal Cell M-current in Weak Pyramidal/Interneuronal Gamma Cluster Formation. In 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), Innopolis, Russia, IEEE. doi:10.1109/DCNAIR50402.2020.9216942

Zakharov, D., Krupa, M., Gutkin, B. & Kuznetsov, A. (2018). High-frequency forced oscillations in neuronlike elements. Physical Review E , 97(6). doi:10.1103/PhysRevE.97.062211

Zakharov, D., Lapish, C., Gutkin, B. & Kuznetsov, A. (2016). Synergy of AMPA and NMDA Receptor Currents in Dopaminergic Neurons: A Modeling Study. Frontiers in computational neuroscience, 10, 48. doi:10.3389/fncom.2016.00048

Wu, J., Gao, M., Shen, J., Shi, W., Oster, A. & Gutkin, B. (2013). Cortical control of VTA function and influence on nicotine reward. Biochemical Pharmacology, 86(8), 1173-1180. doi:10.1016/j.bcp.2013.07.013

Volk, D., Dubinin, I., Gutkin, B., Myasnikova, A. & Nikulin, V. (2018). Cross-Frequency Synchrony Analysis. Frontiers in Neuroinformatics , 12, 72. doi:10.3389/fninf.2018.00072

Versteegh, M., Anguera, X., Jansen, A. & Dupoux, E. (2016). The Zero Resource Speech Challenge 2015: Proposed Approaches and Results. , Vol. 81: In SLTU-2016 Procedia Computer Science, 67-72.

Versteegh, M., Thiollière, R., Schatz, T., Cao, X., Anguera, X., Jansen, A. & Dupoux, E. (2015). The Zero Resource Speech Challenge 2015. In INTERSPEECH-2015, 3169-3173.

Varadarajan, B., Khudanpur, S. & Dupoux, E. (2008 ). Unsupervised Learning of Acoustic Subword Units. In Proceedings of ACL-08: HLT, 165-168.

Tuckwell, H., Jost, J. & Gutkin, B. (2009). Inhibition and modulation of rhythmic neuronal spiking by noise. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 80(3). doi:10.1103/PhysRevE.80.031907

Tsuji, S., Fikkert, P., Minagawa-Kawai, Y., Dupoux, E., Filippin, L., Versteegh, M., Hagoort, P. & Cristia, A. (2017). The more, the better? Behavioral and neural correlates of frequent and infrequent vowel exposure. Developmental Psychobiology, 59(5), 603-612. doi:10.1002/dev.21534

Tran-Van-Minh, A., Caze, R., Abrahamsson, T., Cathala, L., Gutkin, B. & Digregorio, D. (2015). Contribution of sublinear and supralinear dendritic integration to neuronal computations. Frontiers in cellular neuroscience, 9, 67. doi:10.3389/fncel.2015.00067

Tolu, S., Eddine, R., Marti, F., David, V., Graupner, M., Pons, S., Baudonnat, M., Husson, M., Besson, M., Reperant, C., Zemdegs, J., Pagès, C., Hay, Y., Lambolez, B., Caboche, J., Gutkin, B., Gardier, A., Changeux, J., Faure, P. & Maskos, U. (2013). Co-activation of VTA da and GABA neurons mediates nicotine reinforcement. Molecular Psychiatry, 18(3), 382-393. doi:10.1038/mp.2012.83

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.

Thual, A., Dancette, C., Karadayi, J., Benjumea, J. & Dupoux, E. (2018). A K-nearest neighbours approach to unsupervised spoken term discovery. In EEE Spoken Language Technology SLT-2018.

Thiollière, R., Dunbar, E., Synnaeve, G., Versteegh, M. & Dupoux, E. (2015 ). A Hybrid Dynamic Time Warping-Deep Neural Network Architecture for Unsupervised Acoustic Modeling. In INTERSPEECH-2015, 3179-3183.

Teichmann, M., Dupoux, E., Kouider, S., Brugières, P., Boissé, M., Baudic, S., Cesaro, P., Peschanski, M. & Bachoud-Levi, A. (2005). The role of the striatum in rule application: the model of Huntington's disease at early stage. Brain, 128(5), 1155-1167. doi:10.1093/brain/awh472

Teichmann, M., Darcy, I., Bachoud-Levi, A. & Dupoux, E. (2009). The role of the striatum in phonological processing. Evidence from early stages of Huntington's disease. Cortex, 45(7), 839-849. doi:10.1016/j.cortex.2008.12.005

Teichmann, M., Dupoux, E., Cesaro, P. & Bachoud-Levi, A. (2008). The role of the striatum in sentence processing: Evidence from a priming study in early stages of Huntington's disease. Neuropsychologia, 46(1), 174-185. doi:10.1016/j.neuropsychologia.2007.07.022

Teichmann, M., Dupoux, E., Kouider, S. & Bachoud-Levi, A. (2006). The role of the striatum in processing language rules: Evidence from word perception in Huntington's disease. Journal of Cognitive Neuroscience, 18(9), 1555-1569

Tegnér, J., Compte, A., Auffray, C., An, G., Cedersund, G., Clermont, G., Gutkin, B., Oltvai, Z., Stephan, K., Thomas, R. & Villoslada, P. (2009). Computational disease modeling - Fact or fiction? BMC Systems Biology, 3. doi:10.1186/1752-0509-3-56

Synnaeve, G. & Dupoux, E. (2016). A temporal coherence loss function for learning unsupervised acoustic embeddings. , Vol. 81: In SLTU-2016 Procedia Computer Science, 95-100.

Synnaeve, G., Dautriche, I., Boerschinger, B., Johnson, M. & Dupoux, E. (2014). Unsupervised word segmentation in context. In Proceedings of 25th International Conference on Computational Linguistics (CoLing), 2326-2334.