Publications

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2017). A bright future for financial agent-based models. arXiv preprint arXiv:1801.08222

Rooy, M., Koukouli, F., Maskos, U. & Gutkin, B. (2018). Nicotinic modulation of hierarchal inhibitory control over prefrontal cortex resting state dynamics: modeling of genetic modification and schizophreniarelated pathology. bioRxiv, 301051. doi:10.1101/301051

Novikov, N. & Gutkin, B. (2018). Role of NMDA conductance in average firing rate shifts caused by external periodic forcing. arXiv , 1812.03609

Martinez-Saito, M. , Konovalov, R. , Piradov, M. , Shestakova, A. , Gutkin, B. & Klucharev, V. (2018). Action in auctions: neural and computational mechanisms of bidding behavior. BioRxiv, 464925. doi:10.1101/464925

Lazarevich, I. , Gutkin, B. & Prokin, I. (2018). Neural activity classification with machine learning models trained on interspike interval series data. arxiv , 1810.03855

Dumont, G. & Gutkin, B. (2018). Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits. arXiv , 1812.03455

Deperrois, N. & Gutkin, B. (2018). Nicotinic and Cholinergic Modulation of Reward Prediction Error Computations in the Ventral Tegmental Area: a Minimal Circuit Model. bioRxiv, 423806. doi:10.1101/423806

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.)

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

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

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

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

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

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

Murez, M., Smortchkova, J. & Strickland, B. (2020). The mental files theory of singular thought: A psychological perspective. In R. Goodman, J. Genone and N. Kroll (Eds.), Singular Thought and Mental FilesOxford University Press

Mercier, H. & Sperber, D. (2020). Bounded reason in a social world. In Ricardo Viale (Eds.), Routledge Handbook of Bounded Rationality

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

Falck, A., Strickland, B. & Jacob, P. (2022). Social Cognition and Moral Evaluation in Early Human Childhood. In O. Houdé & G. Borst (Eds.), The Cambridge Handbook of Cognitive Development (Cambridge: Cambridge University Press ed., pp. 269-298). doi:10.1017/9781108399838.016

Mercier, H. (2021). Not Born Yesterday: The Science of Who We Trust And What We Believe.

Gutkin, B. & Ahmed, S. (2012). Computational Neuroscience of Drug Addiction.

Mercier, H. & Sperber, D. (2021). L'Énigme de la raison.

Cristia, A. & Peperkamp, S. (2012). Generalizing without encoding specifics: Infants infer phonotactic patterns on sound classes. In Proceedings of the 36th Annual Boston University Conference on Language Development, 126-138.

Caze, R., Humphries, M., Gutkin, B. & Schultz, S. (2013). A difficult classification for neurons without dendrites. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, San Diego, CA, USA, IEEE, 215-218. doi:10.1109/NER.2013.6695910

Seidl, A., Warlaumont, A. & Cristia, A. (2019). Towards detection of canonical babbling by citizen scientists: Performance as a function of clip length. In Proceedings of Interspeech, Graz, Austria.

Schuller, B., Batliner, A., Bergler, C., Pokorny, F., Krajewski, J., Cychosz, M., Vollmann, R., Roelen, S., Schnieder, S., Bergelson, E. & Cristia, A. (2019). The INTERSPEECH 2019 Computational Paralinguistics Challenge: Styrian Dialects, Continuous Sleepiness, Baby Sounds & Orca Activity. In Proceedings of Interspeech, Graz, Austria.

Ryant, N., Church, K., Cieri, C., Cristia, A., Ganapathy, S. & Liberman, M. (2019). Second DIHARD Diarization Challenge: Dataset, task, and baselines. In Proceedings of Interspeech, Graz, Austria.

Loukatou, G., Moran, S., Blasi, D., Stoll, S. & Cristia, A. (2019). Is word segmentation child’s play in all languages? In Proceedings of the Association for Computational Linguistics.

Loukatou, G., Le Normand, M. & Cristia, A. (2019). Is it easier to segment words from infant-directed speech? Modeling evidence from an ecological French corpus. In Proceedings of the 41st Conference of Cognitive Science Society..

Havron, N., Babineau, M., Fiévet, A., de Carvalho, A. & Christophe, A. (2019). Young Children Build Syntactic Predictions During Language Processing and Use Them to Learn Novel-Word Meanings. In The 44th Annual Boston University Conference on Language Development, Boston, USA.

Futaisi Al, N., Zhang, Z., Cristia, A., Warlaumont, A. & Schuller, B. (2019). VCMNet: Weakly Supervised Learning for Automatic Infant Vocalisation Maturity Analysis. In 2019 International Conference on Multimodal Interaction, 205-209.