Publications Theoretical Neuroscience Group
C. Horvat, J.-P. Pfister.
On gauge freedom, conservativity and in-trinsic dimensionality estimation in diffusion models (paper). ICLR 1-17, 2024 pdf
On gauge freedom, conservativity and in-trinsic dimensionality estimation in diffusion models (paper). ICLR 1-17, 2024 pdf
H.-A. Shen, J.P. Pfister, R. Graczyk.
Optimal Activation Functions via Causal Wyner-Ziv Coding. IEEE Information Theory Workshop (ITW) 1-5, 2024 DOI
Optimal Activation Functions via Causal Wyner-Ziv Coding. IEEE Information Theory Workshop (ITW) 1-5, 2024 DOI
H.-A. Shen, S.M. Moser and J.P Pfister.
A Generalization of the Equal Coding Theorem. IEEE Information Theory Workshop (ITW) 329-334, 2023 DOI pdf
A Generalization of the Equal Coding Theorem. IEEE Information Theory Workshop (ITW) 329-334, 2023 DOI pdf
C. Horvat, J.P. Pfister.
Density estimation on low-dimensional manifolds: an inflation-deflation approach. J. Mach. Learn. Res. 61:1-37, 2023 pdf
Density estimation on low-dimensional manifolds: an inflation-deflation approach. J. Mach. Learn. Res. 61:1-37, 2023 pdf
C. Gontier, S.C. Surace, I. Delvendahl, M. Müller, J.P. Pfister.
Efficient sampling-based Bayesian Active Learning for synaptic characterization. PLoS Comput Biol 19(8): e1011342, 2023 DOI pdf
Efficient sampling-based Bayesian Active Learning for synaptic characterization. PLoS Comput Biol 19(8): e1011342, 2023 DOI pdf
E. Abedi, J.P. Pfister and S.C Surace.
A Unifying Theorem for Weighted and Unweighted Particle Filters. SIAM Journal on Control and Optimization 60(2), 597-619, 2022 DOI pdf
A Unifying Theorem for Weighted and Unweighted Particle Filters. SIAM Journal on Control and Optimization 60(2), 597-619, 2022 DOI pdf
C. Horvat, J.P. Pfister.
Intrinsic dimensionality estimation using Normalizing Flows. Advances in Neural Information Processing Systems (NeurIPS) 35:12225-12236, 2022 pdf
Intrinsic dimensionality estimation using Normalizing Flows. Advances in Neural Information Processing Systems (NeurIPS) 35:12225-12236, 2022 pdf
J. Jegminat, J.P. Pfister and S.C Surace.
Learning as filtering: implications for spike-based plasticity. PLoS Comput Biol 18 (2):e100972, 2022 DOI pdf
Learning as filtering: implications for spike-based plasticity. PLoS Comput Biol 18 (2):e100972, 2022 DOI pdf
H.-A. Shen, S. Moser, J.P. Pfister.
The Geometry of Uncoded Transmission for Symmetric Continuous Log-Concave Distributions. IZS 29-33, 2022 pdf
The Geometry of Uncoded Transmission for Symmetric Continuous Log-Concave Distributions. IZS 29-33, 2022 pdf
H.-A. Shen, S.M. Moser†, J.-P. Pfister.
Rate-Distortion Problems of the Poisson Process: a Group-Theoretic Approach. IEEE Information Theory Workshop (ITW) 2021 DOI pdf
Rate-Distortion Problems of the Poisson Process: a Group-Theoretic Approach. IEEE Information Theory Workshop (ITW) 2021 DOI pdf
H.-A. Shen, S.M. Moser, J.-P. Pfister.
Sphere Covering for Poisson Processes. IEEE Information Theory Workshop (ITW) 2021 DOI pdf
Sphere Covering for Poisson Processes. IEEE Information Theory Workshop (ITW) 2021 DOI pdf
L. Aitchison, J. Jegminat, J.A. Menendez, J.P. Pfister, A. Pouget & P.E. Latham .
Synaptic plasticity as Bayesian inference. Nat. Neurosci. 2021 DOI pdf
Synaptic plasticity as Bayesian inference. Nat. Neurosci. 2021 DOI pdf
S.C. Surace, A. Kutschireiter and J.P. Pfister.
Asymptotically exact unweighted particle filter for manifold-valued hidden states and point process observations. IEEE Control Systems Letters 1907.10143, 2020 DOI pdf
Asymptotically exact unweighted particle filter for manifold-valued hidden states and point process observations. IEEE Control Systems Letters 1907.10143, 2020 DOI pdf
J. Jegminat, M. Jastrzebowska, M.V. Pachai, M.H. Herzog, J.P. Pfister.
Bayesian regression explains how human participants handle parameter uncertainty. PLoS Comput Biol 16(5): 1-23, 2020 DOI pdf
Bayesian regression explains how human participants handle parameter uncertainty. PLoS Comput Biol 16(5): 1-23, 2020 DOI pdf
J.P. Pfister, A. Ghosh.
Generalized priority-based model for smartphone screen touches. Phys. Rev. E 102(012307), 1-11, 2020 DOI pdf
Generalized priority-based model for smartphone screen touches. Phys. Rev. E 102(012307), 1-11, 2020 DOI pdf
C. Gontier, J.P. Pfister.
Identifiability of a Binomial Synapse. Front. Comput.Neurosci. 2020 DOI pdf
Identifiability of a Binomial Synapse. Front. Comput.Neurosci. 2020 DOI pdf
S.C. Surace, J.P. Pfister, W. Gerstner, J. Brea.
On the choice of metric in gradient-based theories of brain function. PLoS Comput Biol 16(4): 1-13, 2020 DOI pdf
On the choice of metric in gradient-based theories of brain function. PLoS Comput Biol 16(4): 1-13, 2020 DOI pdf
M. Gilson, J.P. Pfister.
Propagation of Spiking Moments in Linear Hawkes Networks. SIAM J Appl Dyn Syst 19(2):828-859, 2020 DOI pdf
Propagation of Spiking Moments in Linear Hawkes Networks. SIAM J Appl Dyn Syst 19(2):828-859, 2020 DOI pdf
A. Kutschireiter, S.C. Surace, J.P. Pfister.
The Hitchhiker's Guide to Nonlinear Filtering. J. Math Psychology 94, 1-21, 2020 DOI pdf
The Hitchhiker's Guide to Nonlinear Filtering. J. Math Psychology 94, 1-21, 2020 DOI pdf
S.C. Surace, A. Kutschireiter and J.P. Pfister.
How to avoid the curse of dimensionality: scalability of particle filters with and without importance weights. SIAM Review 61(1), 79-91, 2019 DOI pdf
How to avoid the curse of dimensionality: scalability of particle filters with and without importance weights. SIAM Review 61(1), 79-91, 2019 DOI pdf
O.S. Bykowska, C. Gontier, A.L Sax, D.W. Jia, M. Llera-Montero, A.D. Bird, C.J. Houghton, J.P. Pfister and R.P. Costa1.
Model-based inference of synaptic transmission. Front. Synaptic Neurosci. in press, 2019 DOI
Model-based inference of synaptic transmission. Front. Synaptic Neurosci. in press, 2019 DOI
S.C. Surace, J.P. Pfister.
Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes. IEEE Transactions on Automatic Control 64(7), 2814-2829, 2019 DOI pdf
Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes. IEEE Transactions on Automatic Control 64(7), 2814-2829, 2019 DOI pdf
C. Henning, J. von Oswald, J. Sacramento, S.C. Surace, J.P. Pfister and B.F. Grewe.
Approximating the Predictive Distribution via Adversarially-Trained Hypernetworks. 2018 pdf
Approximating the Predictive Distribution via Adversarially-Trained Hypernetworks. 2018 pdf
A. Kutschireiter, S.C. Surace, H. Sprekeler & J.P. Pfister.
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception. Sci Rep 7: 8722, 2017 DOI pdf
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception. Sci Rep 7: 8722, 2017 DOI pdf
S.C. Surace, J.P. Pfister.
A Statistical Model for In Vivo Neuronal Dynamics. PLoS ONE 10(11): e0142435, 2015 DOI pdf
A Statistical Model for In Vivo Neuronal Dynamics. PLoS ONE 10(11): e0142435, 2015 DOI pdf
SM Blom, J.P. Pfister, M. Santello, W. Senn & T. Nevian.
Nerve injury-induced neuropathic pain causes disinhibition of the anterior cingulate cortex. J. Neurosci. 34(17):5754-5764, 2014 DOI pdf
Nerve injury-induced neuropathic pain causes disinhibition of the anterior cingulate cortex. J. Neurosci. 34(17):5754-5764, 2014 DOI pdf
W. Senn, J.P. Pfister.
Reinforcement learning in cortical networks in Encyclopedia of Computational Neuroscience, Springer Encyclopedia of Computational Neuroscience 2014 DOI pdf
Reinforcement learning in cortical networks in Encyclopedia of Computational Neuroscience, Springer Encyclopedia of Computational Neuroscience 2014 DOI pdf
W. Senn, J.P. Pfister.
Spike-Timing-Dependent Plasticity, Learning Rules in Encyclopedia of Computational Neuroscience, Springer Encyclopedia of Computational Neuroscience 2014 DOI pdf
Spike-Timing-Dependent Plasticity, Learning Rules in Encyclopedia of Computational Neuroscience, Springer Encyclopedia of Computational Neuroscience 2014 DOI pdf
J. Brea, W. Senn, J.P. Pfister.
Matching Recall and Storage in Sequence Learning with Spiking Neural Networks. J. Neurosci. 33(23): 9565–9575, 2013 DOI pdf
Matching Recall and Storage in Sequence Learning with Spiking Neural Networks. J. Neurosci. 33(23): 9565–9575, 2013 DOI pdf
J. Gjorgjieva, C. Clopath, J. Audet, and J.P. Pfister.
A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations. PNAS 1-6, 2011 DOI pdf
A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations. PNAS 1-6, 2011 DOI pdf
J. Brea, W. Senn, and J.-P. Pfister.
Sequence learning with hidden units in spiking neural networks. Advances in Neural Information Processing Systems (NeurIPS) pp. 1422-1430, 2011 pdf
Sequence learning with hidden units in spiking neural networks. Advances in Neural Information Processing Systems (NeurIPS) pp. 1422-1430, 2011 pdf
Peer-reviewed conference article - Advances in Neural Information Processing Systems 24 (NIPS 2011)
G. Hennequin, W. Gerstner, J. Pfister.
STDP in adaptive neurons gives close-to-optimal information transmission. Front. Comput.Neurosci. 4:22, 2010 DOI pdf
STDP in adaptive neurons gives close-to-optimal information transmission. Front. Comput.Neurosci. 4:22, 2010 DOI pdf
J.P. Pfister, P.A. Tass.
STDP in oscillatory recurrent networks: theoretical conditions for desynchronization and applications to deep brain stimulation. Front. Comput.Neurosci. 4:22, 2010
STDP in oscillatory recurrent networks: theoretical conditions for desynchronization and applications to deep brain stimulation. Front. Comput.Neurosci. 4:22, 2010
J.P. Pfister, P. Dayan, M. Lengyel.
Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials. Nat. Neurosci. 2010
Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials. Nat. Neurosci. 2010
J.P. Pfister, P. Dayan, M. Lengyel.
Know Thy Neighbour: A Normative Theory of Synaptic Depression in Advances in Neural Information Processing Systems22, edited by Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williamsand A. Culotta, MIT Press, Cambridge MA, 1464-1472, 2009 pdf
Know Thy Neighbour: A Normative Theory of Synaptic Depression in Advances in Neural Information Processing Systems22, edited by Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williamsand A. Culotta, MIT Press, Cambridge MA, 1464-1472, 2009 pdf
T. Toyoizumi , J.P. Pfister, K. Aihara, W. Gerstner.
Optimality Model of Unsupervised Spike-Timing Dependent Plasticity: synaptic memory and weight distribution. Neural Computation 19(3):639-671, 2007 pdf
Optimality Model of Unsupervised Spike-Timing Dependent Plasticity: synaptic memory and weight distribution. Neural Computation 19(3):639-671, 2007 pdf
J.P. Pfister, W. Gerstner.
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects. Advances in Neural Information Processing Systems 18, edited by Y. Weiss and B. Schoellkopf and J. Platt, MIT Press, Cambridge MA, 1083-1090. 2006
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects. Advances in Neural Information Processing Systems 18, edited by Y. Weiss and B. Schoellkopf and J. Platt, MIT Press, Cambridge MA, 1083-1090. 2006
J.P. Pfister, T. Toyoizumi, K. Aihara, W. Gerstner.
Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. Neural Computation 18:1309-1339, 2006
Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. Neural Computation 18:1309-1339, 2006
J.P. Pfister, W. Gerstner.
Triplets of Spikes in a Model of Spike-Timing-Dependent Plasticity. J. Neurosci. 26:9673-9682, 2006 pdf
Triplets of Spikes in a Model of Spike-Timing-Dependent Plasticity. J. Neurosci. 26:9673-9682, 2006 pdf
T. Toyoizumi, J.P. Pfister, K. Aihara, W. Gerstner.
Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. Proceedings of the National Academy of Science USA, 102, 5239-5244. 2005 pdf
Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. Proceedings of the National Academy of Science USA, 102, 5239-5244. 2005 pdf
T. Toyoizumi, J.P. Pfister, K. Aihara, W. Gerstner.
Spike-Timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model.Advances in Neural Information Processing Systems 17, edited by L.K. Saul and Y.Weiss and L. Bottou, MIT Press, Cambridge MA, 1409-1416. 2005 pdf
Spike-Timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model.Advances in Neural Information Processing Systems 17, edited by L.K. Saul and Y.Weiss and L. Bottou, MIT Press, Cambridge MA, 1409-1416. 2005 pdf
J.P. Pfister, D. Barber, W. Gerstner.
Optimal Hebbian Learning: A Probabilistic Point of View. Arti cial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, edited by O. Kaynak, E. Alpaydin, E. Oja and L. Xu. Berlin: Springer-Verlag, 92-98. 2003 pdf
Optimal Hebbian Learning: A Probabilistic Point of View. Arti cial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, edited by O. Kaynak, E. Alpaydin, E. Oja and L. Xu. Berlin: Springer-Verlag, 92-98. 2003 pdf