Publications

Muller, L., Brette, R. & Gutkin, B. (2011). Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking. Frontiers in Computational Neuroscience, 15(5), 45. doi:10.3389/fncom.2011.00045

Myers, C., Sheynin, J., Balsdon, T., Luzardo, A., Beck, K., Hogarth, L., Haber, P. & Moustafa, A. (2016). Probabilistic reward- and punishment-based learning in opioid addiction: Experimental and computational data. Behavioural brain research, 296, 240-248. doi:10.1016/j.bbr.2015.09.018

Nair, A. , B. Johnson, E. , Gregory, S. , Osborne-Crowley, K. , Zeun, P. , Scahill, R. , Lowe, J. , Papoutsi, M., Palminteri, S., Rutledge, R. , Rees, G. & Tabrizi, S. (2021). Aberrant Striatal Value Representation in Huntington's Disease Gene Carriers 25 Years Before Onset. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 910-918. doi:10.1016/j.bpsc.2020.12.015

Najar, A., Bonnet, E., Bahador, B. & Palminteri, S. (2020). The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning. PLoS Biology, 18 (12), e3001028. doi:10.1371/journal.pbio.3001028

Najar, A., Sigaud, O. & Chetouani , M. (2020). Interactively shaping robot behaviour with unlabeled human instructions. Auton Agent Multi-Agent Syst, 34(35). doi:10.1007/s10458-020-09459-6

Neri, P. (2021). Optimal templates for signal extraction by noisy ideal detectors and human observers. Journal of Computational Neuroscience, 49(1-20). doi:10.1007/s10827-020-00768-z

Neri, P. (2020). Complex visual analysis of ecologically relevant signals in Siamese fighting fish. Anim Cogn ., 23(1), 41-53. doi:10.1007/s10071-019-01313-x

Neri, P. (2018). The empirical characteristics of human pattern vision defy theoretically-driven expectations . PLoS Computational Biology , 14(12), e1006585. doi:10.1371/journal.pcbi.1006585

Neri, P. (2017). Object segmentation controls image reconstruction from natural scenes. PLoS biology, 15(8), e1002611. doi:10.1371/journal.pbio.1002611

Norman-Haignere, S., Mcdermott, J., Kanwisher, N. & Conway, B. (2019). Divergence in the functional organization of human and macaque auditory cortex revealed by fMRI responses to harmonic tones. Nature Neuroscience , 22, 1057–1060. doi:10.1038/s41593-019-0410-7

Novikov, N., Zakharov, D., Moiseeva, V. & Gutkin, B. (2021). Activity Stabilization in a Population Model of Working Memory by Sinusoidal and Noisy Inputs. Front. Neural Circuits. doi:10.3389/fncir.2021.647944

Novikov, N. & Gutkin, B. (2020). Role of synaptic nonlinearity in persistent firing rate shifts caused by external periodic forcing. PHYSICAL REVIEW E, 101(5), 052408. doi:10.1103/PhysRevE.101.052408

Novikov, N. & Gutkin, B. (2018). and Gamma Oscillations in Working Memory Functions. Psychology. Journal of Higher School of Economics, 15(1), 174-182

Novikov, N. & Gutkin, B. (2016). Robustness of persistent spiking to partial synchronization in a minimal model of synaptically driven self-sustained activity. Physical review. E, 94(5-1), 052313. doi:10.1103/PhysRevE.94.052313

Oster, A. & Gutkin, B. (2011). A reduced model of DA neuronal dynamics that displays quiescence, tonic firing and bursting. Journal of Physiology Paris, 105, 53-58. doi:10.1016/j.jphysparis.2011.07.012

Oster, A., Faure, P. & Gutkin, B. (2015). Mechanisms for multiple activity modes of VTA dopamine neurons. Frontiers in computational neuroscience, 9, 95. doi:10.3389/fncom.2015.00095

Ota, K. , Wu, Q. & Mamassian, P. (2020). Visual cue estimation with non-gaussian distribution. Journal of vision, 20(11). doi:10.1167/jov.20.11.1436

Otto, T. & Mamassian, P. (2017). Multisensory decisions: The test of a race model, its logic, and power. Multisensory Research, 30(1), 1-24. doi:10.1163/22134808-00002541

Palminteri, S. & Maël, L. (2021). Context-dependent outcome encoding in human reinforcement learning. Current Opinion in Behavioral Sciences, 41, 144-151. doi:10.1016/j.cobeha.2021.06.006

Palminteri, S. & Chevallier, C. (2018). Can We Infer Inter-Individual Differences in Risk-Taking From Behavioral Tasks? Frontiers in psychology, 9, 2307. doi:10.3389/fpsyg.2018.02307

Palminteri, S., Wyart, V. & Koechlin, E. (2017). The Importance of Falsification in Computational Cognitive Modeling. Trends in Cognitive Sciences, 21(6), 425-433. doi:10.1016/j.tics.2017.03.011

Palminteri, S., Lefebvre, G., Kilford, E. & Blakemore, S. (2017). Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing. PLoS computational biology, 13(8), e1005684. doi:10.1371/journal.pcbi.1005684

Palminteri, S. & Pessiglione, M. (2016). Opponent brain systems for reward and punishment learning: Causal evidence from drug and lesion studies in humans. Decision Neuroscience: An Integrative Perspective, 291-303. doi:10.1016/B978-0-12-805308-9.00023-3

Palminteri, S., Kilford, E., Coricelli, G. & Blakemore, S. (2016). The Computational Development of Reinforcement Learning during Adolescence. PLoS Computational Biology, 12(6). doi:10.1371/journal.pcbi.1004953

Paraouty, N., Ewert, S., Wallaert, N. & Lorenzi, C. (2016). Interactions between amplitude modulation and frequency modulation processing: Effects of age and hearing loss. The Journal of the Acoustical Society of America, 140(1), 121. doi:10.1121/1.4955078

Pischedda, D. , Palminteri, S. & Giorgio, C. (2020). The effect of counterfactual information on outcome value coding in medial prefrontal and cingulate cortex: from an absolute to a relative neural code. Journal of Neuroscience, 40(16), 3268-3277. doi:10.1523/JNEUROSCI.1712-19.2020

Pischedda, D. , Palminteri, S. & Giorgio, C. (2020). The role of counterfactual information on outcome value coding in mPFC and cingulate cortex: from an absolute to an adaptive neural code. . The Journal of Neuroscience, 40 (16), 3268-3277. doi:10.1523/JNEUROSCI.1712-19.2020

Ponsot, E., Varnet, L., Wallaert, N., Daoud, E., Shamma, S., Lorenzi, C. & Neri, P. (2021). Mechanisms of Spectrotemporal Modulation Detection for Normal- and Hearing-Impaired Listeners. Trends Hear, 25, 2331216520978029. doi:10.1177/2331216520978029

Ponsot, E. & Neri, P. (2020). Inter-individual differences in spectrotemporal modulation processing despite similar audiograms : could they be used to assess supra-threshold hearing distortions? In e-Forum Acusticum 2020. doi:10.48465/fa.2020.0808

Ponsot, E., Burred, J., Belin, P. & Aucouturier, J. (2018). Reply to Knight et al.: The complexity of inferences from speech prosody should be addressed using data-driven approaches. Proceedings of the National Academy of Sciences of the United States of America, 115(27), E6104-E6105. doi:10.1073/pnas.1806857115