Jean-Pascal Pfister
Jean-Pascal Pfister
Professor of Theoretical Neuroscience, Institute of Neuroinformatics, University of Zurich and ETH
Verified email at - Homepage
Cited by
Cited by
Triplets of spikes in a model of spike timing-dependent plasticity
JP Pfister, W Gerstner
Journal of Neuroscience 26 (38), 9673-9682, 2006
Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning
JP Pfister, T Toyoizumi, D Barber, W Gerstner
Neural computation 18 (6), 1318-1348, 2006
A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations
J Gjorgjieva, C Clopath, J Audet, JP Pfister
Proceedings of the National Academy of Sciences 108 (48), 19383-19388, 2011
Nerve injury-induced neuropathic pain causes disinhibition of the anterior cingulate cortex
SM Blom, JP Pfister, M Santello, W Senn, T Nevian
Journal of Neuroscience 34 (17), 5754-5764, 2014
Generalized Bienenstock–Cooper–Munro rule for spiking neurons that maximizes information transmission
T Toyoizumi, JP Pfister, K Aihara, W Gerstner
Proceedings of the National Academy of Sciences 102 (14), 5239-5244, 2005
Matching recall and storage in sequence learning with spiking neural networks
J Brea, W Senn, JP Pfister
Journal of neuroscience 33 (23), 9565-9575, 2013
Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials
JP Pfister, P Dayan, M Lengyel
Nature neuroscience 13 (10), 1271-1275, 2010
Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution
T Toyoizumi, JP Pfister, K Aihara, W Gerstner
Neural computation 19 (3), 639-671, 2007
Synaptic plasticity as Bayesian inference
L Aitchison, J Jegminat, JA Menendez, JP Pfister, A Pouget, PE Latham
Nature neuroscience 24 (4), 565-571, 2021
STDP in oscillatory recurrent networks: theoretical conditions for desynchronization and applications to deep brain stimulation
JP Pfister, PA Tass
Frontiers in computational neuroscience 4, 2010
Sequence learning with hidden units in spiking neural networks
J Brea, W Senn, JP Pfister
Advances in neural information processing systems 24, 2011
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception
A Kutschireiter, SC Surace, H Sprekeler, JP Pfister
Scientific reports 7 (1), 8722, 2017
How to avoid the curse of dimensionality: Scalability of particle filters with and without importance weights
SC Surace, A Kutschireiter, JP Pfister
SIAM review 61 (1), 79-91, 2019
STDP in adaptive neurons gives close-to-optimal information transmission
G Hennequin, W Gerstner, JP Pfister
Frontiers in Computational Neuroscience 4, 143, 2010
Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model
T Toyoizumi, JP Pfister, K Aihara, W Gerstner
Advances in neural information processing systems 17, 2004
Beyond pair-based STDP: A phenomenological rule for spike triplet and frequency effects
JP Pfister, W Gerstner
Advances in neural information processing systems 18, 2005
Optimal hebbian learning: A probabilistic point of view
JP Pfister, D Barber, W Gerstner
International Conference on Artificial Neural Networks, 92-98, 2003
Online maximum-likelihood estimation of the parameters of partially observed diffusion processes
SC Surace, JP Pfister
IEEE transactions on automatic control 64 (7), 2814-2829, 2018
Denoising normalizing flow
C Horvat, JP Pfister
Advances in Neural Information Processing Systems 34, 9099-9111, 2021
Approximating the predictive distribution via adversarially-trained hypernetworks
C Henning, J von Oswald, J Sacramento, SC Surace, JP Pfister, ...
Yarin, 2018
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