Publications

Findling, C., Skvortsova , V., Dromnelle, R. , Palminteri, S. & Wyart, V. (2019). Computational noise in reward-guided learning drives behavioral variability in volatile environments. Nature Neuroscience , 22, 2066–2077. doi:10.1101/439885

Ladenbauer, J. & Obermayer, K. (2019). Weak electric fields promote resonance in neuronal spiking activity: Analytical results from two-compartment cell and network models. PLOS Computational Biology, 15(4), e1006974. doi:10.1371/journal.pcbi.1006974

Lebreton, M., Bacily , K., Palminteri, S. & Engelmann, J. (2019). Contextual influence on confidence judgments in human reinforcement learning. PLoS Comput Biol, 15(4), e1006973. doi:10.1371/journal.pcbi.1006973

Lebreton, M., Bavard, S., Daunizeau, J. & Palminteri, S. (2019). Assessing inter-individual differences with task-related functional neuroimaging. Nature Human Behaviour. doi:10.1038/s41562-019-0681-8

Fontanesi , L., Palminteri, S. & Lebreton, M. (2019). Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: A meta-analytical approach using diffusion decision modeling. Cognitive Affective Behavioral Neuroscience, 1-13. doi:10.3758/s13415-019-00723-1

Azzalini, D., Rebollo, I. & Tallon-Baudry, C. (2019). Visceral Signals Shape Brain Dynamics and Cognition. Trends in Cognitive Sciences , 23(6), 488-509. doi:10.1016/j.tics.2019.03.007

Lefebvre, G., Nioche, A., Bourgeois-Gironde, S. & Palminteri, S. (2018). Contrasting temporal difference and opportunity cost reinforcement learning in an empirical money-emergence paradigm. PNAS, 115(49), E11446-E11454. doi:10.1073/pnas.1813197115

Bimbard, C., Ledoux, E. & Ostojic, S. (2016). Instability to a heterogeneous oscillatory state in randomly connected recurrent networks with delayed interactions. Physical review. E, 94(6-1), 062207. doi:10.1103/PhysRevE.94.062207

Maravall, M., Ostojic, S., Pressnitzer, D. & Chait, M. (2018). More than the Sum of its Parts: Perception and Neuronal Underpinnings of Sequence Processing. Neuroscience, in press. doi:10.1016/j.neuroscience.2018.07.043

Stefanini, F. , A. Kheirbek, M. , Kushnir, L., C. Jimenez, J. , H. Jennings, J. , D. Stuber, G. , Hen, R. & Fusi, S. (2018). A distributed neural code in the dentate gyrus and in CA1. . doi:10.1101/292953

Kushnir, L. & Fusi, S. (2018). Neural Classifiers with Limited Connectivity and Recurrent Readouts. Journal of Neuroscience, 38 (46), 9900-9924. doi:10.1523/JNEUROSCI.3506-17.2018

Michel, M. , Beck, D. , .., .., King, J. , Kouider, S., Tallon-Baudry, C. & Yoshida , M. (2019). Opportunities and challenges for a maturing science of consciousness. Nature Human Behaviour, 3, 104-107. doi:10.1038/s41562-019-0531-8

Babo-Rebelo, M., Buot, A. & Tallon-Baudry, C. (2019). Neural responses to heartbeats distinguish self from other during imagination. NeuroImage, 191, 10-20. doi:10.1016/j.neuroimage.2019.02.012

Tallon-Baudry, C., Florence, C., Hyeong-Dong, P. & Babo-Rebelo, M. (2018). The neural monitoring of visceral inputs, rather than attention, accounts for first-person perspective in conscious vision. Cortex, . doi:10.1016/j.cortex.2017.05.019

Babo-Rebelo, M., Richter, C. & Tallon-Baudry, C. (2016). Neural Responses to Heartbeats in the Default Network Encode the Self in Spontaneous Thoughts. The Journal of Neuroscience, 36(30), 7829-7840. doi:10.1523/JNEUROSCI.0262-16.2016

Babo-Rebelo, M., Wolpert, N., Adam, C., Hasboun, D. & Tallon-Baudry, C. (2016). Is the cardiac monitoring function related to the self in both the default network and right anterior insula? Philosophical Transactions B, 371(1708), 20160004. doi:10.1098/rstb.2016.0004

Schaffer, E., Ostojic, S. & Abbott, L. (2013). A complex-valued firing-rate model that approximates the dynamics of spiking networks. PLoS computational biology, 9(10), e1003301. doi:10.1371/journal.pcbi.1003301

Ostojic, S., Brunel, N. & Hakim, V. (2009). How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains. The Journal of Neuroscience, 29(33), 10234-53. doi:10.1523/JNEUROSCI.1275-09.2009

Ostojic, S. & Brunel, N. (2011). From spiking neuron models to linear-nonlinear models. PLoS Computational Biology, 7(1), e1001056. doi:10.1371/journal.pcbi.1001056

Ostojic, S. (2011). Interspike interval distributions of spiking neurons driven by fluctuating inputs. Journal of neurophysiology, 106(1), 361-73. doi:10.1152/jn.00830.2010

Saez, A., Rigotti, M., Ostojic, S., Fusi, S. & Salzman, C. (2015). Abstract Context Representations in Primate Amygdala and Prefrontal Cortex. Neuron, 87(4), 869-81. doi:10.1016/j.neuron.2015.07.024

Ostojic, S. (2014). Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons. Nature Neuroscience, 17(4), 594-600. doi:10.1038/nn.3658

Ostojic, S., Szapiro, G., Schwartz, E., Barbour, B., Brunel, N. & Hakim, V. (2015). Neuronal morphology generates high-frequency firing resonance. The Journal of Neuroscience, 35(18), 7056-68. doi:10.1523/JNEUROSCI.3924-14.2015

Graupner, M., Wallisch, P. & Ostojic, S. (2016). Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate. The Journal of Neuroscience, 36(44), 11238-11258. doi:10.1523/JNEUROSCI.0104-16.2016

Mastrogiuseppe, F. & Ostojic, S. (2017). Intrinsically-generated fluctuating activity in excitatory-inhibitory networks. PLoS computational biology, 13(4), e1005498. doi:10.1371/journal.pcbi.1005498

Correa, C. , Noorman, S. , Jiang, J. , Palminteri, S., Cohen, M. , Lebreton, M. & van Gaal, S. (2018). How the level of reward awareness changes the computational and electrophysiological signatures of reinforcement learning. Journal of Neuroscience , 0457-18. doi:10.1523/JNEUROSCI.0457-18.2018

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

Bavard, S., Lebreton, M., Khamassi, M., Coricelli, G. & Palminteri, S. (2018). Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences. Nature Communications, 9(4503)

Mastrogiuseppe, F. & Ostojic, S. (2018). Linking connectivity, dynamics and computations in recurrent neural networks. Neuron, 99(3), 609-623. doi:DOI:https://doi.org/10.1016/j.neuron.2018.07.003

Martí, D., Brunel, N. & Ostojic, S. (2018). Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks. Physical Review E, 97, 062314. doi:https://doi.org/10.1103/PhysRevE.97.062314