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 2024

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

eLIFE 2023

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)