Selected Publications

Evolving to learn: discovering interpretable plasticity rules for spiking networks. (pdf )

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

arXiv 2020

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

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 )

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

arXiv NIPS 2018, 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 )

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