Selected Publications

Learning beyond sensations: how dreams organize neuronal representations. (pdf , DOI , arXiv )

N. Deperrois, M.A. Petrovici, W. Senn, J. Jordan

Neuroscience & Biobehavioral Reviews 2023

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways. (pdf , DOI , bioRxiv )

W.A.M. Wybo, M.C Tsai, V.A.K. Tran, B. Illing, J. Jordan, A. Morrison, W. Senn

PNAS 2023

A Neuronal Least-action Principle For Real-time Learning In Cortical Circuits. (pdf , DOI , bioRxiv )

W. Senn, Dold D., Kungl A.F., Ellenberger B, Jordan J., Bengio Y., Sacramento J. and Petrovici M.A

eLIFE 2024

Learning cortical representations through perturbed and adversarial dreaming. (pdf , DOI , arXiv )

N. Deperrois, M.A. Petrovici, W. Senn, and J. Jordan

eLIFE 2022

Natural-gradient learning for spiking neurons. (pdf , DOI , arXiv )

E. Kreutzer, W. Senn, M.A. Petrovici

eLIFE 2022

Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses. (pdf , DOI )

W.A.M. Wybo, J. Jordan, B. Ellenberger, U. Marti Mengual, T. Nevian, W. Senn

eLIFE 10:e60936, 2021

Evolving interpretable plasticity for spiking networks. (pdf , DOI , arXiv )

J. Jordan, M. Schmidt, W. Senn, M.A. Petrovici

eLIFE 2021

Fast and energy-efficient neuromorphic deep learning with first-spike times. (pdf , DOI , arXiv )

J. Göltz, L. Kriener, A. Baumbach, S. Billaudelle, O. Breitwieser, B. Cramer, D. Dold, A.F. Kungl, W. Senn, J. Schemmel, K. Meier, M.A. Petrovici

Nature Machine Intelligence 823–835, 2021

Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons. (pdf , arXiv )

P. Haider, B. Ellenberger, L. Kriener, J. Jordan, W. Senn, M.A. Petrovici

Advances in Neural Information Processing Systems (NeurIPS) 2021

Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks. (pdf , DOI )

T. Mesnard, G. Vignoud, J. Sacramento, W. Senn, Y. Bengio

arXiv 2019

Lagrangian neurodynamics for real-time error-backpropagation across cortical areas. (pdf )

D. Dold, A.F. Kungl, J. Sacramento, M.A. Petrovici, K. Schindler, J. Binas, Y. Bengio, W. Senn

2019

Dendritic cortical microcircuits approximate the backpropagation algorithm. (pdf , arXiv )

J. Sacramento, R.P. Costa, Y. Bengio, W. Senn

Advances in Neural Information Processing Systems (NeurIPS) 2018

Prospective Coding by Spiking Neurons. (pdf , DOI )

J. Brea, A. Gaál, R. Urbanczik †, W. Senn

PLoS Comput Biol 12(6): e100500, 2016

Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites. (pdf , DOI )

M. Schiess, R. Urbanczik, W. Senn

PLoS Comput Biol 12(2): e1004638, 2016

Learning by the dendritic prediction of somatic spiking. (pdf , DOI , Supplement )

R. Urbanczik, W. Senn

Neuron 81(3):521–528, 2014

Spatio-Temporal Credit Assignment in Neuronal Population Learning. (pdf , DOI , Supplement )

J. Friedrich, R. Urbanczik, W. Senn

PLoS Comput Biol 7:1-13, 2011

Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity. (pdf , DOI )

I.R. Fiete, W. Senn, C.Z.H. Wang, R.H.R. Hahnloser

Neuron 65:563-576, 2010

Reinforcement learning in populations of spiking neurons. (DOI , Supplement )

R. Urbanczik, W. Senn

Nat. Neurosci. 12:250-252, 2009

Dendritic encoding of sensory stimuli controlled by deep cortical interneurons. (pdf , DOI , Supplement )

M. Murayama, E. Pérez-Garci, T. Nevian, T. Bock, W. Senn, M.E. Larkum

Nature 457:1137-1141, 2009