Publications

Derome, M., Kozuharova, P., O Diaconescu, A., Denève, S., Jardri, R. & Allen, P. (2023). Functional connectivity and glutamate levels of the medial prefrontal cortex in schizotypy are related to sensory amplification in a probabilistic reasoning task. NeuroImage, 278, 120280. doi:10.1016/j.neuroimage.2023.120280

Garcia, B., Lebreton, M., Bourgeois-Gironde, S. & Palminteri, S. (2023). Experiential values are underweighted in decisions involving symbolic options. Nature human behaviour, . doi:10.1038/s41562-022-01496-3

Angeletos Chrysaitis, N. , Jardri, R., Denève, S. & Seriès, P. (2021). No increased circular inference in adults with high levels of autistic traits or autism. Plos Computational Biology, 17(9), e1009006. doi:10.1371/journal.pcbi.1009006

Leptourgos, P., Bouttier, V., Denève, S. & Jardri, R. (2022). From hallucinations to synaesthesia: A circular inference account of unimodal and multimodal erroneous percepts in clinical and drug-induced psychosis. Neuroscience & Biobehavioral Reviews, 135, 104593. doi:10.1016/j.neubiorev.2022.104593

Bouttier, V., Duttagupta, S. , Denève, S. & Jardri, R. (2022). Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes. Schizophrenia Research, 245, 59-67. doi:10.1016/j.schres.2020.12.045

Lussange, J., Vrizzi, S., Bourgeois-Gironde, S., Palminteri, S. & Gutkin, B. (2022). Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model. Comput Econ. doi:10.1007/s10614-022-10249-3

Pacherie, E., Le Bars, S., Bourgeois-Gironde, S., Wyart, V., Sari, I. & Chambon, V. (2022). Motor coordination and strategic cooperation in joint action. Psychological science, 33(5), 736-751. doi:10.1177/09567976211053275

Csaba Farkas, B. , Jacquet, P. & Chambon, V. (2022). Do perceived control and time orientation mediate the effect of early life adversity on reproductive behaviour and health status? Insights from the European Value Study and the European Social Survey. Humanities and Social Sciences Communications, 9(52). doi:10.1057/s41599-022-01066-y

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.

Batty, M., Kovarski, K. & Meaux, E. (2018). De l’exploration à la perception des visages chez les personnes avec un trouble du spectre de l’autisme. Neuropsychologie et remédiations des troubles du spectre de l’autismeEditions deboeck Supérieur

Brendel, W. , Bourdoukan, R., Vertechi, P. , Machens, C. & Denève, S. (2020). Learning to represent signals spike by spike. PLOS Computational Biology, 16(3), e1007692. doi:10.1371/journal.pcbi.1007692

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

Caze, R., Humphries, M. & Gutkin, B. (2013). Modulation of computational capacity of neurons due to dendritic synaptic interactions. PLoS Comput. Biol., 9(2)

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.

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

Chambon, V., Thero, H., Vidal, M., Vandendriessche, H., Haggard, P. & Palminteri, S. (2020). Information about action outcomes differentially affects learning from self-determined versus imposed choices. Nature Human Behaviour , 4, 1067-1079. doi:10.1038/s41562-020-0919-5

Chambon, V., Farrer, C., Pacherie, E., Leboyer, M., Zalla, T. & Jacquet, P. (2017). Reduced Sensitivity to Social Priors During Action Prediction in Adults with Autism Spectrum Disorders. Cognition, 160, 17–26 . doi:10.1016/j.cognition.2016.12.005

Chambon, V., Domenech, P., Jacquet, P., Barbalat, G., Bouton, S., Pacherie, E., Koechlin, E. & Farrer, C. (2017). Neural coding of prior expectations in hierarchical intention inference. Scientific reports, 7(1), 1278. doi:10.1038/s41598-017-01414-y

Denève, S., Alemi, A. & Bourdoukan, R. (2017). The Brain as an Efficient and Robust Adaptive Learner. Neuron, 94(5), 969-977. doi:10.1016/j.neuron.2017.05.016

Dipoppa, M. & Gutkin, B. (2013). Flexible frequency control of cortical oscillations enables computations required for working memory. Proceedings of the National Academy of Sciences of the United States of America, 110(31), 12828-12833. doi:10.1073/pnas.1303270110

Dipoppa, M. & Gutkin, B. (2013). Correlations in background activity control persistent state stability and allow execution of working memory tasks. Frontiers in Computational Neuroscience, 7, 139. doi:10.3389/fncom.2013.00139

Dipoppa, M., Krupa, M., Torcini, A. & Gutkin, B. (2012). Marginally Stable States and Quasi-periodic minor attractors in excitable pulse-coupled networks. SIADS , 63(1), 62-97

Fontolan, L., Krupa, M., Hyafil, A. & Gutkin, B. (2013). Analytical insights on theta-gamma coupled neural oscillators. Journal of Mathematical Neuroscience, 3(1), 1-20. doi:10.1186/2190-8567-3-16

Graupner, M., Maex, R. & Gutkin, B. (2013). Endogenous Cholinergic Inputs and Local Circuit Mechanisms Govern the Phasic Mesolimbic Dopamine Response to Nicotine. PLoS Computational Biology, 9(8). doi:10.1371/journal.pcbi.1003183

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.)

Gutierrez, G. & Denève, S. (2018). Population adaptation in efficient balanced networks. eLife, 8, e46926. doi:10.7554/eLife.46926

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

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

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

Hyafil, A., Fontolan, L., Kabdebon, C., Gutkin, B. & Giraud, A. (2015). Speech encoding by coupled cortical theta and gamma oscillations. eLife, 4, 1-45. doi:10.7554/eLife.06213