Publications

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

Jazayeri, M. & Ostojic, S. (2021). Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity. Current Opinion in Neurobiology, 70, 113-120. doi:10.1016/j.conb.2021.08.002

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

Krupa, M., Gielen, S. & Gutkin, B. (2014). Intrinsic and synaptic mechanisms for clustered cortical gamma. J Comput. Neurosci , 37(2), 357-76

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

Buchin, A., Rieubland, S., Häusser, M., Gutkin, B. & Roth, A. (2016). Inverse Stochastic Resonance in Cerebellar Purkinje Cells. PLoS computational biology, 12(8), e1005000. doi:10.1371/journal.pcbi.1005000

Simeone, O. , Rajendran, B. , Gruning, A. , Eleftheriou, E. , Davies, M. , Denève, S. & Huang, G. (2019). Learning Algorithms and Signal Processing for Brain-Inspired Computing. IEEE Signal Processing Magazine, 36(6), 12-15. doi:10.1109/MSP.2019.2935557

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2021). Learning and Cognition in Financial Markets: A Paradigm Shift for Agent-Based Models. Advances in Intelligent Systems and Computing (Vol. 1252, pp. 241-255). doi:10.1007/978-3-030-55190-2_19

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.

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

Salem-Garcia, N., Palminteri, S. & Lebreton, M. (2023). Linking confidence biases to reinforcement-learning processes. Psychological review, . doi:10.1037/rev0000424

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

Dumont, G. & Gutkin, B. (2018). Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits. arXiv , 1812.03455

Dumont, G., Ermentrout, G. & Gutkin, B. (2017). Macroscopic phase-resetting curves for spiking neural networks. Physical review. E, 96, 042311. doi:10.1103/PhysRevE.96.042311

Mansouri, F., Koechlin, E., Rosa, M. & Buckley, M. (2017). Managing competing goals - a key role for the frontopolar cortex. Nature reviews. Neuroscience, 18(11), 645-657. doi:10.1038/nrn.2017.111

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

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

Deperrois, N., Moiseeva, V. & Gutkin, B. (2019). Minimal Circuit Model of Reward Prediction Error Computations and Effects of Nicotinic Modulations. Frontiers in neural circuits, 12, 116. doi:10.3389/fncir.2018.00116

Keramati, M., Ahmed, S. & Gutkin, B. (2017). Misdeed of the need: towards computational accounts of transition to addiction. Current opinion in neurobiology, 46, 142-153. doi:10.1016/j.conb.2017.08.014

Krupa, M., Gutkin, B. & Zakharov, D. (2020). Modeling dopaminergic modulation of clustered gamma rhythms. Communications in Nonlinear Science and Numerical Simulation, 82(105086), 1-10. doi:10.1016/j.cnsns.2019.105086

Graupner, M. & Gutkin, B. (2009). Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms. Acta Pharmacologica Sinica, 30(6), 681-693. doi:10.1038/aps.2009.87

Graupner, M., Ahmed, S. & Gutkin, B. (2009). Modelling dynamics of addiction. Pharmacopsychiatry, 42(Suppl 1), S144-52

Lussange, J., Lazarevich, I. , Bourgeois-Gironde, S., Palminteri, S. & Gutkin, B. (2020). Modelling Stock Markets by Multi-agent Reinforcement Learning. Computational Economics, 57, 113-147. doi:10.1007/s10614-020-10038-w

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

Lourenço, J. , Michela De Stasi,, A. , Deleuze, C. , Bigot, M. , Pazienti, A. , Aguirre, A. , Giugliano, M. , Ostojic, S. & Bacci, A. (2019). Modulation of coordinated activity across cortical layers by plasticity of inhibitory synapses onto layer 5 pyramidal neurons. Cell Reports, 3, 630-641.e5. doi:10.1016/j.celrep.2019.12.052

Lourenço, J. , De Stasi, A., Deleuze, C. , Bigot, M. , Pazienti, A. , Aguirre, A. , Giugliano, M., Ostojic, S. & Bacci, A. (2020). Modulation of Coordinated Activity across Cortical Layers by Plasticity of Inhibitory Synapses. Cell reports, 30(3), 630-641.e5. doi:10.1016/j.celrep.2019.12.052

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

Dogonasheva, O., Kasatkin, D., Gutkin, B. & Zakharov, D. (2022). Multistability and evolution of chimera states in a network of type II Morris-Lecar neurons with asymmetrical nonlocal inhibitory connections. Chaos (Woodbury, N.Y.), 32(10), 101101. doi:10.1063/5.0117845

Dogonasheva, O., Gutkin, B. & Zakharov, D. (2021). Multistability of coherent states in ring networks of type II neurons with asymmetrical nonlocal inhibitory connectivity. Journal of Computational Neuroscience. doi:10.1016/j.chaos.2021.111541

Graupner, M., Wallisch, P. & Ostojic, S. (2019). Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate. Journal of Neuroscience , 36 (44), 11238-11258. doi:10.1523/JNEUROSCI.0104-16.2016