Publications

Cimesa, L., Ciric, L. & Ostojic, S. (2023). Geometry of population activity in spiking networks with low-rank structure. PLoS computational biology, 19(8), e1011315. doi:10.1371/journal.pcbi.1011315

Beiran, M., Meirhaeghe, N., Sohn, H., Jazayeri, M. & Ostojic, S. (2023). Parametric control of flexible timing through low-dimensional neural manifolds. Neuron, 111(5), 739-753.e8. doi:10.1016/j.neuron.2022.12.016

Molano-Mazón, M., Shao, Y., Duque, D., Robert Yang, G., Ostojic, S. & De La Rocha, J. (2023). Recurrent networks endowed with structural priors explain suboptimal animal behavior. Current biology : CB, 33(4), 622-638.e7. doi:10.1016/j.cub.2022.12.044

Shao, Y. & Ostojic, S. (2023). Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks. PLoS computational biology, 19(1), e1010855. doi:10.1371/journal.pcbi.1010855

Barbosa, J., Babushkin, V. , Temudo, A. , Sreenivasan, K. & Compte, A. (2021). Across-Area Synchronization Supports Feature Integration in a Biophysical Network Model of Working Memory. Front. Neural Circuits. doi:10.3389/fncir.2021.716965

Stein, H., Barbosa, J. & Compte, A. (2021). Towards biologically constrained attractor models of schizophrenia. Current Opinion in Neurobiology, 70, 171-181. doi:10.1016/j.conb.2021.10.013

Joao, B., Lozano-Soldevilla, D. & Compte, A. (2021). Pinging the brain with visual impulses reveals electrically active, not activity-silent, working memories. PLOS Biology, 20(3), e3001603. doi:10.1371/journal.pbio.3001436

Barbosa, J., Stein, H., Zorowitz, S. , Niv, Y. , Summerfield, C., Soto-Faraco, S. & Hyafil, A. (2022). A practical guide for studying human behavior in the lab. Behavior Research Methods. doi:10.3758/s13428-022-01793-9

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

Dubreuil, A., Valente, A., Beiran, M., Mastrogiuseppe, F. & Ostojic, S. (2022). The role of population structure in computations through neural dynamics. Nature Neuroscience, 25, 783–794. doi:10.1038/s41593-022-01088-4

Valente, A., Ostojic, S. & Pillow, J. (2022). Probing the Relationship Between Latent Linear Dynamical Systems and Low-Rank Recurrent Neural Network Models. Neural Computation, 1-22. doi:10.1162/neco_a_01522

K Chillale, R., Shamma, S., Ostojic, S. & Boubenec, Y. (2023). Dynamics and maintenance of categorical responses in primary auditory cortex during task engagement. eLife, 12(e85706). doi:10.7554/eLife.85706

Bagur, S. , Averseng, M., Elgueda, D., David, S., Fritz, J., Yin, P., Shamma, S., Boubenec, Y. & Ostojic, S. (2018). Go/No-Go task engagement enhances population representation of target stimuli in primary auditory cortex. Nature Communications, 9, 2529 . doi:10.1038/s41467-018-04839-9

Batty, M., Kovarski, K. & Meaux, E. (2018). De l’exploration à la perception des visages chez les personnes avec un trouble du spectre de l’autisme. Neuropsychologie et remédiations des troubles du spectre de l’autismeEditions deboeck Supérieur

Beiran, M., Dubreuil, A., Valente, A., Mastrogiuseppe, F. & Ostojic, S. (2021). Shaping dynamics with multiple populations in low-rank recurrent networks. Neural Computation, 33(6), 1572-1615. doi:10.1162/neco_a_01381 Article history

Beiran, M. & Ostojic, S. (2019). Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks . PLoS Computational Biology, 15(3), e1006893. doi:10.1371/journal.pcbi.1006893

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

Bondanelli, G. & Ostojic, S. (2020). Coding with transient trajectories in recurrent neural networks. PLoS Computational Biology, 16 (2), e1007655. doi:10.1371/journal.pcbi.1007655

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

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. (2013). Dendrites enhance both single neuron and network computation. In Remme et al (eds) (Eds.), Dendritic ComputationSpringer

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

Dubreuil, A., Valente, A., Mastrogiuseppe, F. & Ostojic, S. (2019). Disentangling the roles of dimensionality and cell classes in neural computation. In NeurIPS Workshop.

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

Giulio, B., Thomas, D., Brice, B. & Ostojic, S. (2021). Network dynamics underlying OFF responses in the auditory cortex. eLife, 10, e53151. doi:10.7554/eLife.53151

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

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