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Gontier, Camille; Surace, Simone Carlo; Delvendahl, Igor; Müller, Martin; Pfister, Jean-Pascal (2023). Efficient sampling-based Bayesian Active Learning for synaptic characterization. PLoS computational biology, 19(8), e1011342. Public Library of Science 10.1371/journal.pcbi.1011342
Horvat, Christian; Pfister, Jean-Pascal (2023). Density estimation on low-dimensional manifolds: an inflation-deflation approach. Journal of Machine Learning Research, 24(61) Microtome Publishing
Jegminat, Jannes; Surace, Simone Carlo; Pfister, Jean-Pascal (2022). Learning as filtering: Implications for spike-based plasticity. PLoS computational biology, 18(2), e1009721. Public Library of Science 10.1371/journal.pcbi.1009721
Abedi, Ehsan; Surace, Simone Carlo; Pfister, Jean-Pascal (2022). A Unification of Weighted and Unweighted Particle Filters. SIAM journal on control and optimization, 60(2), pp. 597-619. Society for Industrial and Applied Mathematics 10.1137/20M1382404
Aitchison, Laurence; Jegminat, Jannes; Menendez, Jorge Aurelio; Pfister, Jean Pascal; Pouget, Alexandre; Latham, Peter E. (2021). Synaptic plasticity as Bayesian inference. Nature neuroscience, 24(4), pp. 565-571. Nature America 10.1038/s41593-021-00809-5
Shen, Hui-An; Moser, Stefan M.; Pfister, Jean-Pascal (2021). Rate-Distortion Problems of the Poisson Process: a Group-Theoretic Approach. In: 2021 IEEE Information Theory Workshop (ITW) (pp. 1-6). IEEE 10.1109/ITW48936.2021.9611405
Horvat, Christian; Pfister, Jean-Pascal (2021). Denoising Normalizing Flow. In: NeurIPS Proceedings. 2021.
Surace, Simone Carlo; Kutschireiter, Anna; Pfister, Jean Pascal (2020). Asymptotically Exact Unweighted Particle Filter for Manifold-Valued Hidden States and Point Process Observations. IEEE Control Systems Letters, 4(2), pp. 480-485. 10.1109/LCSYS.2019.2951093
Kutschireiter, Anna; Surace, Simone Carlo; Pfister, Jean Pascal (2020). The Hitchhiker’s guide to nonlinear filtering. Journal of Mathematical Psychology, 94(102307), p. 102307. 10.1016/j.jmp.2019.102307
Gontier, Camille; Pfister, Jean-Pascal (2020). Identifiability of a Binomial Synapse. Frontiers in computational neuroscience, 14 Frontiers Research Foundation 10.3389/fncom.2020.558477
Pfister, Jean-Pascal; Ghosh, Arko (2020). Generalized priority-based model for smartphone screen touches. Physical review. E - statistical, nonlinear, and soft matter physics, 102(1-1), 012307. American Physical Society 10.1103/PhysRevE.102.012307
Jegminat, Jannes; Jastrzębowska, Maya A.; Pachai, Matthew V.; Herzog, Michael H.; Pfister, Jean-Pascal (2020). Bayesian regression explains how human participants handle parameter uncertainty. PLoS computational biology, 16(5), e1007886. Public Library of Science 10.1371/journal.pcbi.1007886
Gilson, Matthieu; Pfister, Jean-Pascal (2020). Propagation of Spiking Moments in Linear Hawkes Networks. SIAM journal on applied dynamical systems, 19(2), pp. 828-859. Society for Industrial and Applied Mathematics 10.1137/18M1220030
Surace, Simone Carlo; Pfister, Jean-Pascal; Gerstner, Wulfram; Brea, Johanni (2020). On the choice of metric in gradient-based theories of brain function. PLoS computational biology, 16(4), e1007640. Public Library of Science 10.1371/journal.pcbi.1007640
Bykowska, Ola; Gontier, Camille; Sax, Anne-Lene; Jia, David W.; Montero, Milton Llera; Bird, Alex D.; Houghton, Conor; Pfister, Jean-Pascal; Ponte Costa, Rui (2019). Model-Based Inference of Synaptic Transmission. Frontiers in synaptic neuroscience, 11, p. 21. Frontiers Research Foundation 10.3389/fnsyn.2019.00021
Surace, Simone Carlo; Kutschireiter, Anna; Pfister, Jean-Pascal (2019). How to Avoid the Curse of Dimensionality: Scalability of Particle Filters with and without Importance Weights. SIAM review, 61(1), pp. 79-91. SIAM 10.1137/17M1125340
Surace, Simone Carlo; Pfister, Jean-Pascal (2019). Online Maximum-Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes. IEEE transactions on automatic control, 64(7), pp. 2814-2829. IEEE 10.1109/TAC.2018.2880404
Kutschireiter, Anna; Surace, Simone Carlo; Sprekeler, Henning; Pfister, Jean Pascal (2017). Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception. Scientific Reports, 7(1), p. 8722. Nature Publishing Group 10.1038/s41598-017-06519-y
Surace, Simone Carlo; Pfister, Jean Pascal (2015). A Statistical Model for In Vivo Neuronal Dynamics. PLoS ONE, 10(11), e0142435. Public Library of Science 10.1371/journal.pone.0142435
Senn, Walter; Pfister, Jean Pascal (2014). Spike-Timing Dependent Plasticity, Learning Rules. In: Jaeger, Dieter; Jung, Ranu (eds.) Encyclopedia of Computational Neuroscience (pp. 1-10). New York: Springer 10.1007/978-1-4614-7320-6_683-1
Senn, Walter; Pfister, Jean Pascal (2014). Reinforcement learning in cortical networks. In: Jaeger, Dieter; Jung, Ranu (eds.) Encyclopedia of Computational Neuroscience (pp. 1-6). New York: Springer 10.1007/978-1-4614-7320-6_580-1
Blom, Sigrid Marie; Pfister, Jean Pascal; Santello, Mirko; Senn, Walter; Nevian, Thomas (2014). Nerve Injury-Induced Neuropathic Pain Causes Disinhibition of the Anterior Cingulate Cortex. Journal of neuroscience, 34(17), pp. 5754-5764. Society for Neuroscience 10.1523/JNEUROSCI.3667-13.2014
Brea, Johanni Michael; Senn, Walter; Pfister, Jean Pascal (2013). Matching Recall and Storage in Sequence Learning with Spiking Neural Networks. Journal of neuroscience, 33(23), pp. 9565-9575. Society for Neuroscience 10.1523/JNEUROSCI.4098-12.2013
Gjorgjieva, Julijana; Clopath, Claudia; Audet, Juliette; Pfister, Jean Pascal (2011). A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations. Proceedings of the National Academy of Sciences of the United States of America - PNAS, 108(48), pp. 19383-19388. Washington, D.C.: National Academy of Sciences NAS 10.1073/pnas.1105933108
Pfister, Jean Pascal; Lengyel, Mate (2011). Interactions between short-term and long-term plasticity: shooting for a moving target. Nature Precedings NPG 10.1038/npre.2011.5857.1
