Publications

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

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

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

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.

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.

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.

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

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

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

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

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

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.)

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

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

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