Publications
Articles
A Unifying Theorem for Weighted and Unweighted Particle Filters.
Abedi E., Pfister J.P. and Surace S.C. (2022)
SIAM Journal on Control and Optimisation, 60 (2), 597-619 [Journal], [PDF]
The Geometry of Uncoded Transmission for Symmetric Continuous Log-Concave Distributions.
Shen H.A., Moser S.M. and Pfister J.P. (2022)
2022 International Zurich Seminar on Information and Communication, 29-33 [Journal], [PDF]
Learning as filtering: implications for spike-based plasticity.
Jegminat J., Surace S.C. and Pfister J.P. (2022)
Plos Computational Biology, 18 (2) :e1009721. [Journal], [PDF],
Denoising Normalizing Flow.
Horvat C. and Pfister J.P. (2021)
Advances in Neural Information Processing Systems 34, 1-9, [Journal], [PDF], [SUPP]
Rate-Distortion Problems of the Poisson Process: a Group-Theoretic Approach.
Shen H.A., Moser S.M. and Pfister J.P. (2021)
2021 IEEE Information Theory Workshop, 1-5, [Journal], [PDF]
Sphere Covering for Poisson Processes.
Shen H.A., Moser S.M. and Pfister J.P. (2021)
2020 IEEE Information Theory Workshop, 1-5, [Journal], [PDF]
Synaptic plasticity as Bayesian inference.
Aitchison L., Jegminat J., Menendez J.A., Pfister J.P., Pouget A and Latham P.E. (2021)
Nature Neuroscience, 24(4), 565 - 571, [Journal], [PDF], [SUPP]
Identifiability of a Binomial Synapse.
Gontier C. and Pfister J.P. (2020)
Frontiers in Computational Neuroscience, 14(558477), 1-16, [Journal], [PDF]
A generalized priority-based model for smartphone screen touches.
Pfister, J.P. and Ghosh, A. (2020)
Physical Review E, 102(012307), 1-11 [Journal], [PDF], [Data and Code]
Bayesian regression explains how human participants handle parameter uncertainty.
Jegminat J., Jastrzebowska M., Pachai M., Herzog M.H. and Pfister J.P. (2020)
Plos Computational Biology 16(5), 1-23 [Journal], [PDF]
On the choice of metric in gradient-based theories of brain function.
Surace S.C, Gerstner W., Pfister J.P. and Brea J. (2020)
Plos Computational Biology, 16(4), 1-13 [Journal] [PDF]
The Hitchhiker's Guide to Nonlinear Filtering.
Kutschireiter A. Surace, S.C and Pfister, J.P. (2020)
Journal of Mathematical Psychology, 94, 1-21. [Journal] [PDF]
Propagation of Spiking Moments in Linear Hawkes Networks.
Gilson M. and Pfister J.P. (2020)
SIAM Journal on Applied Dynamical Systems, 19(2), 828-859 [Journal] [PDF]
Asymptotically exact unweighted particle filter for manifold-valued hidden states and point process observations.
Surace, S.C Kutschireiter A. and Pfister, J.P. (2020)
IEEE Control Systems Letters, 4(2), 480-485. [Journal] [PDF]
Model-based inference of synaptic transmission.
Bykowska O., Gontier C., Sax A.-L., Jia D., Llera-Montero M., Bird A.D., Houghton C., Pfister J.-P. and Costa R.P. (2019).
Frontiers in Synaptic Neuroscience, 11(21), 1-9. [Journal][PDF]
Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes.
Surace, S.C and Pfister, J.P. (2019)
IEEE Transactions on Automatic Control, 64(7), 2814-2829. [Journal], [PDF]
How to avoid the curse of dimensionality: scalability of particle filters with and without importance weights.
Surace S.C., Kutschireiter A. and Pfister, J.P. (2019)
SIAM Review, 61(1), 79-91 [Journal], [PDF]
Approximating the Predictive Distribution via Adversarially-Trained Hypernetworks.
Henning C., von Oswald J., Sacramento J., Surace, S.C., Pfister J.P. and Grewe B.F. (2018)
Bayesian Deep Learning Workshop, NeurIPS (Spotlight) [Workshop], [PDF]
Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains.
Kutschireiter, A. and Pfister, J.P. (2018)
arXiv:1804.09739 [ArXiv], [PDF]
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.
Kutschireiter, A., Surace, S.C., Sprekeler, H, and Pfister, J.P. (2017)
Nature Scientific Report, 7(1), 8722. [Journal], [PDF]
Optimised information gathering in smartphone users.
Ghosh, A., Pfister, J. P., and Cook, M. (2017)
arXiv:1701.02796 [ArXiv], [PDF]
A statistical model for in vivo neuronal dynamics.
Surace, S.C. and Pfister, J.P. (2015)
PLoS ONE, 10(11): e0142435[Journal], [PDF]
Spike-Timing Dependent Plasticity, Learning Rules.
Senn W. and Pfister J.P. (2014)
Encyclopedia of Computational Neuroscience, Springer, 2825-2832 [Journal], [Preprint]
Reinforcement learning in cortical networks.
Senn W. and Pfister J.P. (2014)
Encyclopedia of Computational Neuroscience, Springer, 2611-2617 [Journal], [Preprint]
Nerve Injury-Induced Neuropathic Pain Causes Disinhibition of the Anterior Cingulate Cortex.
Blom* S.M., Pfister* J.P., Santello M., Senn W. and Nevian T. (2014)
Journal of Neuroscience, 34(17), 5754-5764. [Journal], [PDF]
Matching Recall and Storage in Sequence Learning with Spiking Neural Networks.
Brea J., Senn W. and Pfister J.P. (2013)
Journal of Neuroscience, 33(23), 9565-9575. [Journal], [PDF]
-> Press commentary: [Unibe News]
A triplet spike-timing-dependent plasticity model generalizes the Bienenstock-Cooper-Munro rule to higher-order spatiotemporal correlations.
Gjorgjieva J., Clopath C., Audet J. and Pfister J.P. (2011)
Proceedings of the National Academy of Science USA, 108 (48) 19383-19388 [Journal], [PDF]
-> Press commentary: [Uniaktuell]
Sequence learning with hidden units in spiking neural networks
Brea J., Senn W. and Pfister J.P. (2011)
Advances in Neural Information Processing Systems, 24, edited by J. Shawe-Taylor, R.S. Zemel, P. Bartlett, F.C.N. Pereira and K.Q. Weinberger, 1422-1430 [PDF]
STDP in Adaptive Neurons gives close-to-optimal Information Transmission
Hennequin G., Gerstner W. and Pfister J.P. (2010)
Frontiers in Computational Neuroscience 4, 22. [Journal], [PDF]
Synapses with short-term plasticity
are optimal estimators of presynaptic membrane potentials
Pfister J.P., Dayan P. and Lengyel M. (2010)
Nature Neuroscience. 13 (10), 1271-1275. [Journal], [PDF], [Supp. Info]
-> [F1000 factor]
STDP in oscillatory recurrent networks: theoretical conditions for desynchronization and applications to deep brain stimulation
Pfister J.P. and Tass P.A. (2010)
Frontiers in Computational Neuroscience 4, 22. [Journal], [PDF]
Know Thy Neighbour: A Normative Theory of Synaptic Depression
Pfister J.P., Lengyel M. and Dayan P. (2009)
Advances in Neural Information Processing Systems 22, edited by Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams
and A. Culotta, MIT Press, Cambridge MA, 1464-1472. [PDF], [Supp. Info]
Optimality Model of Unsupervised
Spike-Timing Dependent Plasticity: Synaptic Memory and Weight Distribution
Toyoizumi T., Pfister J.P., Aihara K. and Gerstner W. (2007)
Neural Computation, 19, 639-671. [PDF]
Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity
Pfister J.P. and Gerstner W. (2006)
Journal of Neuroscience, 26, 9673-9682. [PDF], [high resolution figures], [corrigendum]
Optimal Spike-Timing Dependent Plasticity for Precise Action
Potential Firing in Supervised Learning
Pfister J.P., Toyoizumi T., Barber D. and Gerstner W.
(2006)
Neural Computation, 18, 1309-1339. [PDF]
Beyond Pair-Based STDP: a Phenomenological Rule for Spike
Triplet and Frequency Effects
Pfister J.P. and Gerstner W. (2006)
Advances in Neural Information Processing Systems 18, Y. Weiss and B.
Schölkopf
and J. Platt, MIT Press, Cambridge MA, 1083-1090 [PDF]
Generalized Bienenstock-Cooper-Munro rule for spiking neurons that
maximizes information transmission
Toyoizumim T., Pfister J.P., Aihara A. and Gerstner W. (2005)
PNAS, 102, 5239 5244. [PDF], [Supp. Info],[sup. fig. 6],[sup. fig. 7]
Spike-Timing Dependent Plasticity and Mutual Information Maximization
for a Spiking Neuron Model
Toyoizumi T., Pfister J.P., Aihara K. and Gerstner W.(2005)
Advances in Neural Information Processing Systems 17, L.K. Saul and Y.
Weiss and L.Bottou, MIT Press, Cambridge MA, 1409-1416. [PDF]
Optimal Hebbian Learning: A Probabilistic Point of View
Pfister J.P., Barber D. and Gerstner W. (2003)
In ICANN Proceedings, O. Kaynak, E. Alpaydin, E. Oja and L. Xu,
Springer, 92-98. [PDF]
Thesis
Theory of Non-linear Spike-Time-Dependent Plasticity
Pfister J.P.(2006)
PhD Thesis. [PDF]
Technical reports
Classification of EEG signals a comparaison between SVM and
Diffusive
Neural Networks
Pfister J.P. and Gerstner W.(2002)
Diploma project [PDF]
Classification of EEG signals with Support Vector Machine (SVM)
Pfister J.P. and Gerstner W.(2001)
Semester project [PDF]
Désambiguïsation d'un Dictionnaire de Synonymes
Pfister J.P., Chappelier J.C. and Rajman M.
(2001)
project [PDF]
Magnetic Field Distribution in a Stepping Motor
Pfister J.P. Oudet C. (2000)
project
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