Triplets of spikes in a model of spike timing-dependent plasticity JP Pfister, W Gerstner Journal of Neuroscience 26 (38), 9673-9682, 2006 | 749 | 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 | 305 | 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 | 223 | 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 | 158 | 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 | 156 | 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 | 133 | 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 | 93 | 2010 |
Synaptic plasticity as Bayesian inference L Aitchison, J Jegminat, JA Menendez, JP Pfister, A Pouget, PE Latham Nature neuroscience 24 (4), 565-571, 2021 | 78 | 2021 |
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 | 71 | 2007 |
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 | 58 | 2010 |
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception A Kutschireiter, SC Surace, H Sprekeler, JP Pfister Scientific reports 7 (1), 8722, 2017 | 54 | 2017 |
Sequence learning with hidden units in spiking neural networks J Brea, W Senn, JP Pfister Advances in neural information processing systems 24, 2011 | 52 | 2011 |
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 | 50 | 2019 |
STDP in adaptive neurons gives close-to-optimal information transmission G Hennequin, W Gerstner, JP Pfister Frontiers in Computational Neuroscience 4, 143, 2010 | 44 | 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 | 41 | 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 | 35 | 2005 |
Denoising normalizing flow C Horvat, JP Pfister Advances in Neural Information Processing Systems 34, 9099-9111, 2021 | 31 | 2021 |
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 | 30 | 2018 |
Optimal hebbian learning: A probabilistic point of view JP Pfister, D Barber, W Gerstner International Conference on Artificial Neural Networks, 92-98, 2003 | 30 | 2003 |
On the choice of metric in gradient-based theories of brain function SC Surace, JP Pfister, W Gerstner, J Brea PLoS computational biology 16 (4), e1007640, 2020 | 24 | 2020 |