2021

On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning Marc Aurel Vischer, Robert Tjarko Lange, Henning Sprekeler arRxiv
Self-organization of a doubly asynchronous irregular network state for spikes and bursts Filip Vercruysse, Richard Naud, Henning Sprekeler BioRxiv
Learning excitatory-inhibitory neuronal assemblies in recurrent networks Owen Mackwood, Laura B Naumann, Henning Sprekeler eLife 2021;10:e5971

2020

Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation Michiel Remme, Urs Bergmann, Denis Alevi, Susanne Schreiber, Henning Sprekeler, Richard Kempter BioRxiv
Learning not to learn: Nature versus nurture in silico Robert Tjarko Lange, Henning Sprekeler arRxiv
Interneuron diversity is required for compartment-specific feedback inhibition Joram Keijser, Henning Sprekeler BioRxiv
A thalamocortical top-down circuit for associative memory M Belén Pardi, Johanna Vogenstahl, Tamas Dalmay, Teresa Spanò, De-Lin Pu, Laura B Naumann, Friedrich Kretschmer, Henning Sprekeler, Johannes J Letzkus Science 370:844-848
Learning prediction error neurons in a canonical interneuron circuit Loreen Hertäg, Henning Sprekeler eLife 2020;9:e57541
Presynaptic inhibition rapidly stabilises recurrent excitation in the face of plasticity Laura Bella Naumann, Henning Sprekeler PLoS Comput Biol 16(8): e1008118

2019

Modeling grid fields instead of modeling grid cells Sophie Rosay, Simon Weber, Marcello Mulas Journal of Computational Neuroscience 47(1):43-60
Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types Loreen Hertaeg, Henning Sprekeler PLoS Computational Biology 15 (5), e1006999
A local measure of symmetry and orientation for individual spikes of grid cells Simon Nikolaus Weber, Henning Sprekeler PLoS Computational Biology 15 (2), e1006804

2018

Sparse bursts optimize information transmission in a multiplexed neural code R. Naud, H. Sprekeler PNAS, 115(27):E6329-E6338
Learning place cells, grid cells, and invariances with excitatory and inhibitory plasticity S.N. Weber, H. Sprekeler eLife 2018;7:e34560

2017

Memory replay in balanced recurrent networks N. Chenkov, H. Sprekeler, R. Kempter PLoS Computational Biology 13(1): e1005359
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception A. Kutschireiter, S.C. Surace, H. Sprekeler, J.P. Pfister Scientific Reports 7:8722, DOI:10.1038/s41598-017-06519-y
Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond H. Sprekeler Current Opinion in Neurobiology 43, 198-203

2016

Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons K.A. Wilmes, H. Sprekeler, S. Schreiber PLoS Computational Biology, 12(2), e1004768
Receptive field formation by interacting excitatory and inhibitory plasticity Claudia Clopath, Tim P Vogels, Robert C Froemke, Henning Sprekeler BioRxiv

2015

Inheritance of Hippocampal Place Fields Through Hebbian Learning: Effects of Theta Modulation and Phase Precession of Structure Formation T. D'Albis, J. Jaramillo, H. Sprekeler, R. Kempter Neural Computation, 27(8), 1624-1672

2014

An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation H. Sprekeler, T. Zito and L. Wiskott Journal of Machine Learning Research 15, 921-947

2013

Reinforcement Learning using a Continuous Time Actor-Critic Framework with Spiking Neurons N. Fremaux, H. Sprekeler, W. Gerstner PLoS Computational Biology, 9(4): e1003024
Changing the responses of cortical neurons from sub- to supra-threshold using single spikes in vivo V. Pawlak, D. S. Greenberg, H. Sprekeler, W. Gerstner, J. Kerr eLife 2013;2:e00012

2012

The silent period of evidence integration in fast decision making J. Rüter, H. Sprekeler, W. Gerstner, M. H. Herzog PloS One 8(1):e46525
Theory and simulation in neuroscience W. Gerstner, H. Sprekeler, G. Deco Science 338:60-65
Perceptual learning, Roving & the Unsupervised Bias M. H. Herzog, K. C. Aberg, N. Fremaux, W. Gerstner, H. Sprekeler Vision Research, 61:95-99

2011

Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks T. Vogels*, H. Sprekeler*, F. Zenke, C. Clopath and W. Gerstner Science, 334:1569-1573
Paradoxical evidence integration in rapid decision processes J. Rüter, N. Marcille, H. Sprekeler, W. Gerstner and M. Herzog PLoS Computational Biology, 8(2):e1002382
On the Relation of Slow Feature Analysis and Laplacian Eigenmaps H. Sprekeler Neural Computation 23:3287-3302
A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells H. Sprekeler and L. Wiskott Neural Computation 23:303-335

2010

Functional Requirements for Reward-modulated Spike Timing-Dependent Plasticity N. Fremaux*, H. Sprekeler* and W. Gerstner Journal of Neuroscience 30:13326-13337
Slow Feature Analysis L. Wiskott, P. Berkes, M. Franzius, H. Sprekeler and N. Wilbert Scholarpedia, 6(4):5282

2009

Code-Specific Policy-Gradient Rules for Spiking Neurons H. Sprekeler, G. Hennequin and W.Gerstner Advances in Neural Information Processing Systems 22 (NIPS 2009)

2008

Predictive Coding and the Slowness Principle: An Information-Theoretic Approach F. Creutzig and H. Sprekeler Neural Computation 20:1026-41

2007

Slowness and Sparseness lead to Place, Head-Direction and Spatial-View Cells M. Franzius*, H. Sprekeler* and L. Wiskott Neural Computation 20:1026-41
Slowness: An Objective for Spike-Timing-Dependent Plasticity? H. Sprekeler, C. Michaelis and L. Wiskott PLoS Computational Biology 3(6):e112

2004

Positive Correlations in Tunneling through coupled Quantum Dots G. Kießlich, H. Sprekeler, A. Wacker, and E. Schöll Semiconductor Science and Technology 19, S 37
Coulomb Effects in Tunneling through a Quantum Dot Stack H. Sprekeler, G. Kießlich, A. Wacker, and E. Schöll Phys. Rev. B 69, 125328