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  • Sophie Rosay, Simon Weber, Marcello Mulas (2019)
    Modeling grid fields instead of modeling grid cells  
    Journal of Computational Neuroscience 47(1):43-60
  • Loreen Hertaeg, Henning Sprekeler (2019)
    Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types
    PLoS Computational Biology 15 (5), e1006999

  • Simon Nikolaus Weber, Henning Sprekeler (2019)
    A local measure of symmetry and orientation for individual spikes of grid cells
    PLoS Computational Biology 15 (2), e1006804

  • R. Naud, H. Sprekeler (2018)
    Sparse bursts optimize information transmission in a multiplexed neural code
    PNAS, 115(27):E6329-E6338
  • S.N. Weber, H. Sprekeler (2018)
    Learning place cells, grid cells, and invariances with excitatory and inhibitory plasticity
    eLife 7, e34560
  • A. Kutschireiter, S.C.  Surace, H.  Sprekeler, J.P.  Pfister (2017)
    Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception
    Scientific Reports 7:8722, DOI:10.1038/s41598-017-06519-y
  • H. Sprekeler (2017)
    Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond
    Current Opinion in Neurobiology 43, 198-203
  • N. Chenkov, H. Sprekeler, R. Kempter (2017)
    Memory replay in balanced recurrent networks
    PLoS Computational Biology 13(1): e1005359
  • K.A. Wilmes, H. Sprekeler, S. Schreiber (2016)
    Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons
    PLoS Computational Biology, 12(2), e1004768
  • T. D'Albis, J. Jaramillo, H. Sprekeler, R. Kempter (2015)
    Inheritance of Hippocampal Place Fields Through Hebbian Learning: Effects of Theta Modulation and Phase Precession of Structure Formation
    Neural Computation, 27(8), 1624-1672
  • H. Sprekeler, T. Zito and L. Wiskott (2014)
    An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation
    Journal of Machine Learning Research 15, 921-947
  • N. Fremaux, H. Sprekeler, W. Gerstner (2013)
    Reinforcement Learning using a Continuous Time Actor-Critic Framework with Spiking Neurons
    PLoS Computational Biology, 9(4): e1003024
  • V. Pawlak, D. S. Greenberg, H. Sprekeler, W. Gerstner, J. Kerr (2013)
    Changing the responses of cortical neurons from sub- to supra-threshold using single spikes in vivo
    eLife 2013;2:e00012
  • J. Rüter, H. Sprekeler, W. Gerstner, M. H. Herzog (2012)
    The silent period of evidence integration in fast decision making
    PloS One 8(1):e46525
  • W. Gerstner, H. Sprekeler, G. Deco (2012)
    Theory and simulation in neuroscience
    Science 338:60-65
    pdf on the Science website
  • M. H. Herzog, K. C. Aberg, N. Fremaux, W. Gerstner, H. Sprekeler (2012)
    Perceptual learning, Roving & the Unsupervised Bias
    Vision Research, 61:95-99
    pdf available online
  • T. Vogels*, H. Sprekeler*, F. Zenke, C. Clopath and W. Gerstner (2011)
    Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks
    Science, 334:1569-1573
    look here for a pdf
  • J. Rüter, N. Marcille, H. Sprekeler, W. Gerstner and M. Herzog (2011)
    Paradoxical evidence integration in rapid decision processes
    PLoS Computational Biology, 8(2):e1002382
  • H. Sprekeler (2011)
    On the Relation of Slow Feature Analysis and Laplacian Eigenmaps
    Neural Computation 23:3287-3302
  • H. Sprekeler and L. Wiskott (2011)
    A Theory of Slow Feature Analysis for Transformation-Based Input Signals
    with an Application to Complex Cells

    Neural Computation 23:303-335
  • N. Fremaux*, H. Sprekeler* and W. Gerstner (2010)
    Functional Requirements for Reward-modulated Spike Timing-Dependent Plasticity
    Journal of Neuroscience 30:13326-13337
  • L. Wiskott, P. Berkes, M. Franzius, H. Sprekeler and N. Wilbert (2010)
    Slow Feature Analysis
    Scholarpedia, 6(4):5282
  • H. Sprekeler, G. Hennequin and W.Gerstner (2009)
    Code-Specific Policy-Gradient Rules for Spiking Neurons
    Advances in Neural Information Processing Systems 22 (NIPS 2009)
  • F. Creutzig and H. Sprekeler (2008)
    Predictive Coding and the Slowness Principle: An Information-Theoretic Approach
    Neural Computation 20:1026-41
  • M. Franzius*, H. Sprekeler* and L. Wiskott (2007)
    Slowness and Sparseness lead to Place, Head-Direction and Spatial-View Cells
    PLoS Computational Biology, 3(8):e166
  • H. Sprekeler, C. Michaelis and L. Wiskott (2007)
    Slowness: An Objective for Spike-Timing-Dependent Plasticity?
    PLoS Computational Biology 3(6):e112
  • G. Kießlich, H. Sprekeler, A. Wacker, and E. Schöll (2004)
    Positive Correlations in Tunneling through coupled Quantum Dots
    Semiconductor Science and Technology 19, S 37
    (pdf on cond-mat)
  • H. Sprekeler, G. Kießlich, A. Wacker, and E. Schöll (2004)
    Coulomb Effects in Tunneling through a Quantum Dot Stack
    Phys. Rev. B 69, 125328
    (pdf on cond-mat)




  • Owen Mackwood, Laura B Naumann, Henning Sprekeler (2020)
    Learning excitatory-inhibitory neuronal assemblies in recurrent networks 
    bioRxiv, doi.org/10.1101/2020.03.30.016352
  • Loreen Hertäg, Henning Sprekeler (2020)
    Learning prediction error neurons in a canonical interneuron circuit 
    bioRxiv, https://doi.org/10.1101/2020.02.27.968776 
  • Laura Bella Naumann, Henning Sprekeler (2020)
    Presynaptic inhibition rapidly stabilises recurrent excitation in the face of plasticity 
    bioRxiv, https://doi.org/10.1101/2020.02.11.944082
  • S.N. Weber, H. Sprekeler (2018)
    A local measure of symmetry and orientation for individual spikes of grid cells
    bioRxiv, doi.org/10.1101/425637
    pdf, website
  • L. Hertäg, H. Sprekeler (2018)
    Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types
    bioRxiv, DOI:https://doi.org/10.1101/410340
  • R. Naud, H. Sprekeler
    Burst Ensemble Multiplexing: A Neural Code Connecting Dendritic Spikes with Microcircuits 
    bioRxiv, doi.org/10.1101/143636
  • S.N. Weber, H. Sprekeler
    Learning place cells, grid cells and invariances: A unifying model 
    bioRxiv, doi.org/10.1101/102525
  • C. Clopath, T.P. Vogels, R.C. Froemke, H. Sprekeler
    Receptive field formation by interacting excitatory and inhibitory synaptic plasticity 
    bioRxiv, doi.org/10.1101/066589



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