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


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
Learning not to learn: Nature versus nurture in silico
Robert Tjarko Lange, Henning Sprekeler
Interneuron diversity is required for compartment-specific feedback inhibition
Joram Keijser, Henning Sprekeler
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


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


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


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


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


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


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


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


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


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


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


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


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


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


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