Inhalt des Dokuments
Publikationen
- Sophie Rosay, Simon Weber, Marcello Mulas (2019)
Modeling grid fields instead of modeling grid cells
Journal of Computational Neuroscience 47(1):43-60
pdf - 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
pdf
- 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
pdf
- R. Naud, H. Sprekeler (2018)
Sparse bursts optimize information transmission in a multiplexed neural code
PNAS, 115(27):E6329-E6338
pdf
- S.N. Weber, H. Sprekeler (2018)
Learning place cells, grid cells, and invariances with excitatory and inhibitory plasticity
eLife 7, e34560
pdf
- 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
pdf
- H. Sprekeler (2017)
Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond
Current Opinion in Neurobiology 43, 198-203
pdf
- N. Chenkov, H. Sprekeler, R. Kempter (2017)
Memory replay in balanced recurrent networks
PLoS Computational Biology 13(1): e1005359
pdf
- 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
pdf
- 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
pdf
- 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
pdf
- 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
pdf
- 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
pdf
- 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
pdf
- 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
pdf
- H. Sprekeler (2011)
On the Relation of Slow Feature Analysis and Laplacian Eigenmaps
Neural Computation 23:3287-3302
pdf
- 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
pdf
- N. Fremaux*, H. Sprekeler* and W. Gerstner (2010)
Functional Requirements for Reward-modulated Spike Timing-Dependent Plasticity
Journal of Neuroscience 30:13326-13337
pdf
- L. Wiskott, P. Berkes, M. Franzius, H. Sprekeler and N. Wilbert (2010)
Slow Feature Analysis
Scholarpedia, 6(4):5282
link
- 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)
pdf
- F. Creutzig and H. Sprekeler (2008)
Predictive Coding and the Slowness Principle: An Information-Theoretic Approach
Neural Computation 20:1026-41
pdf
- 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
pdf
- H. Sprekeler, C. Michaelis and L. Wiskott (2007)
Slowness: An Objective for Spike-Timing-Dependent Plasticity?
PLoS Computational Biology 3(6):e112
pdf
- 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)
Preprints
- 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
pdf - Loreen Hertäg, Henning Sprekeler (2020)
Learning prediction error neurons in a canonical interneuron circuit
bioRxiv, https://doi.org/10.1101/2020.02.27.968776
pdf - 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
pdf - 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
pdf - R. Naud, H. Sprekeler
Burst Ensemble Multiplexing: A Neural Code Connecting Dendritic Spikes with Microcircuits
bioRxiv, doi.org/10.1101/143636
pdf - S.N. Weber, H. Sprekeler
Learning place cells, grid cells and invariances: A unifying model
bioRxiv, doi.org/10.1101/102525
pdf - C. Clopath, T.P. Vogels, R.C. Froemke, H. SprekelerReceptive field formation by interacting excitatory and inhibitory synaptic plasticity
bioRxiv, doi.org/10.1101/066589
pdf