Publications

V Kuchibhotla, K. , Hindmarsh Sten, T., C. Papadoyannis, E. , Elnozahy, S. , Fogelson, K. , Chillale, R. , Boubenec, Y., C. Holland, P. , Ostojic, S. & C. Froemke, R. (2019). Dissociating task acquisition from expression during learning reveals latent knowledge. Nature Communications, 10, 2151. doi:10.1038/s41467-019-10089-0

Mastrogiuseppe, F. & Ostojic, S. (2019). A geometrical description of global dynamics in trained feedback networks. Neural Computation, 31(6), 1139-1182. doi:10.1162/neco_a_01187

Ladenbauer, J., McKenzie , S., English , D., Hagens , O. & Ostojic, S. (2019). Inferring and validating mechanistic models of neural microcircuits based on spike-train data. Nature Communications, 10(4933). doi:10.1038/s41467-019-12572-0

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

Safra, L., Chevallier, C. & Palminteri, S. (2019). Depressive symptoms are associated with blunted reward learning in social contexts. PLOS Computational Biology, 15(7), e1007224. doi:10.1371/journal.pcbi.1007224

Safra, L., Palminteri, S. & Chevallier, C. (2019). Social information impairs reward learning in depressive subjects: behavioral and computational characterization. Plos Computational Biology, 15(7), e1007224. doi:10.1101/378281

Sidarus, N., Palminteri, S. & Chambon, V. (2019). Cost-benefit trade-offs in decision-making and learning. PLOS Computational Biology, 15(9), e1007326. doi:10.1371/journal.pcbi.1007326

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

Sidarus, N., Palminteri, S. & Chambon, V. (2018). Trading off the cost of conflict against expected rewards. Plos Computational Biology, 42809. doi:10.1371/journal.pcbi.1007326

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

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

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

Koechlin, E. (2018). Prefrontal function and cognitive control: from action to language. Current Opinion in Behavioral Sciences, 21, 106-111. doi:10.1016/j.cobeha.2018.03.008

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