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

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

Ghambaryan, A., Gutkin, B., Klucharev, V. & Koechlin, E. (2021). Additively Combining Utilities and Beliefs: Research Gaps and Algorithmic Developments. Frontiers in Neuroscience, . doi:10.3389/fnins.2021.704728

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.

Bouchacourt, F., Palminteri, S., Koechlin, E. & Ostojic, S. (2020). Temporal Chunking as a Mechanism for Unsupervised Learning of Task-Sets. eLife, 9, e50469. doi:10.7554/eLife.50469

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, C., Haggard, P. & Palminteri, S. (2020). Choosing and learning: outcome valence differentially affects learning from free versus forced choices. Nature Human Behaviour, .

Chambon, V., Thero, H., Findling, C. & Koechlin, E. (2018). Believing in one's power: a counterfactual heuristic for goal-directed control. bioRxiv, 498675. doi:10.1101/498675

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

Domenech, P., Rheims, S. & Koechlin, E. (2020 ). Neural Mechanisms resolving Exploitation-Exploration dilemmas in the prefrontal cortex. Science, 369(6507), eabb0184. doi:10.1126/science.abb0184

Domenech, P., Redouté, J., Koechlin, E. & Dreher, J. (2018). The Neuro-Computational Architecture of Value-Based Selection in the Human Brain. Cerebral cortex (New York, N.Y. : 1991), 28(2), 585-601. doi:10.1093/cercor/bhw396

Duverne, S. & Koechlin, E. (2017). Rewards and Cognitive Control in the Human Prefrontal Cortex. Cerebral cortex (New York, N.Y. : 1991), 27(10), 5024-5039. doi:10.1093/cercor/bhx210

Findling, C., Chopin, N. & Koechlin, E. (2021). Imprecise neural computations as a source of adaptive behaviour in volatile environments. Nature Human Behaviour , 5, 99-112. doi:10.1038/s41562-020-00971-z

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

Giavazzi, M., Daland, R., Palminteri, S., Peperkamp, S., Brugières, P., Jacquemot, C., Schramm, C., Cleret de Langavant, L. & Bachoud-Levi, A. (2018 ). The role of the striatum in linguistic selection: Evidence from Huntington’s disease and computational modeling. Cortex, 109, 189-204. doi:10.1016/j.cortex.2018.08.031

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).