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Draft:Henning Sprekeler

From Wikipedia, the free encyclopedia
  • Comment: This will need a bit more work on it but thank you for the efforts so far. Please review WP:NACADEMIC to see if there are better criteria that can be readily met? For example any elected fellowships (not fellowship by payment)? Any criticial notices or book reviews? I am a bit triggered by "his group" in the final research paragraph, plus an h-index of 31. Accepting this is a limited research field, we normally want somewhere nearer 50, so it may be a few years WP:TOOSOON. By all means add a few more supporting sources and resubmit for another review. ChrysGalley (talk) 11:44, 12 September 2026 (UTC)


Henning Sprekeler is a German computational neuroscientist. He is professor for Modelling of Cognitive Processes at the Technical University of Berlin, where he holds a Bernstein professorship in the Faculty of Electrical Engineering and Computer Science and is a member of the Bernstein Center for Computational Neuroscience Berlin.[1][2] He is known for the mathematical theory of slow feature analysis and an influential model of inhibitory synaptic plasticity that is implemented as a standard synapse model in neural simulation software and is referred to in the literature as the Vogels–Sprekeler rule.[3][4]

Education and early career

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Sprekeler studied physics at the University of Freiburg and in Berlin.[1][5] His earliest publications, from his time as a physics student, concerned electron transport and correlations in coupled quantum dot systems.[6]

From 2004 to 2008 he carried out doctoral research at the Humboldt University of Berlin under the supervision of Laurenz Wiskott, working on the theory of slow feature analysis and its relation to the self-organisation of sensory representations.[1][5] He then joined the Brain Mind Institute of the École Polytechnique Fédérale de Lausanne as a postdoctoral researcher in the laboratory of Wulfram Gerstner, and subsequently worked with Richard Kempter at the Institute for Theoretical Biology of the Humboldt University of Berlin.[1][5]

Career

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In 2011 Sprekeler received the Bernstein Award in Computational Neuroscience of the German Federal Ministry of Education and Research[5][7] for his contribution to the field of synaptic plasticity. In in 2012 he started a junior research group at the Humboldt University of Berlin, and soon after moved to Cambridge, UK, where he took up a lectureship at the University of Cambridge in 2013. He returned to Berlin in late 2014 as professor for Modelling of Cognitive Processes at the Technical University of Berlin and the Bernstein Center Berlin.[1][8]

At the Technical University of Berlin he is a principal investigator in the German Research Foundation Cluster of Excellence "Science of Intelligence"[9] and was a principal investigator in the Collaborative Research Centre 1315 on memory consolidation, leading a project on neuronal models of schema formation.[10]

Research

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Slow feature analysis

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Slow feature analysis is an unsupervised learning method built on the observation that behaviourally relevant features of the world change more slowly than the sensory signals carrying them: an object's identity persists while the light it casts on the retina changes from instant to instant.[11]

Sprekeler's doctoral and early postdoctoral work, with Wiskott and colleagues, developed the mathematical theory of the method, relating it to Laplacian eigenmap methods[12] and to nonlinear blind source separation,[13] and showing that a biologically plausible form of spike-timing-dependent plasticity can implement the slowness objective at individual synapses.[14] With Mathias Franzius and Wiskott he showed that applying slowness and sparseness to the visual input a rodent would receive while moving through an environment causes representations resembling place cells, head-direction cells and spatial-view cells to emerge, although the model is given no information about position.[15]

Inhibitory synaptic plasticity

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Cortical neurons receive large excitatory and inhibitory inputs that nearly cancel, a condition known as the excitation–inhibition balance; because learning continually alters excitatory connections, how a circuit keeps that balance is not obvious.[16]

During his postdoctoral work at EPFL, Sprekeler co-authored a 2011 paper in Science with Tim Vogels, Friedemann Zenke, Claudia Clopath and Gerstner proposing that inhibitory synapses are themselves plastic, following a rule that uses only locally available signals to adjust inhibition until it offsets the excitation a neuron receives. Memories stored as strongly interconnected groups of neurons are thereby held latent, counterbalanced by inhibition until a cue or a transient reduction of inhibition allows them to reactivate.[17]

The rule has been adopted as a standard component of neural simulation software: the NEST simulator includes a built-in synapse model named vogels_sprekeler_synapse,[3] and an independent reimplementation is maintained as a reference model by the Open Source Brain project.[4] The original model is archived in ModelDB.[18] Inhibitory plasticity and its consequences for the excitation–inhibition balance have been the subject of subsequent review articles in the field,[16][19] and remain a central theme of Sprekeler's own work.[20]

Later work

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Sprekeler continues to contribute to research on reinforcement learning in networks of spiking neurons, characterising the conditions under which reward-modulated spike-timing-dependent plasticity performs gradient ascent on expected reward.[21] Most recently he focused more broadly on the computational roles of the diverse types of cortical inhibitory interneurons, including dendritic inhibition, the formation of prediction-error responses in canonical interneuron circuits, and long-range inhibitory control of neocortical memory;[22] systems memory consolidation; and, within the Science of Intelligence cluster, the analysis of animal behaviour and its relation to machine learning.[23]

Selected publications

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  • Franzius, M.; Sprekeler, H.; Wiskott, L. (2007). "Slowness and sparseness lead to place, head-direction, and spatial-view cells". PLOS Computational Biology. 3 (8) e166. Bibcode:2007PLSCB...3..166F. doi:10.1371/journal.pcbi.0030166. PMC 1963505. PMID 17784780.
  • Sprekeler, H.; Michaelis, C.; Wiskott, L. (2007). "Slowness: an objective for spike-timing-dependent plasticity?". PLOS Computational Biology. 3 (6) e112. Bibcode:2007PLSCB...3..112S. doi:10.1371/journal.pcbi.0030112. PMC 1904380. PMID 17604445.
  • Frémaux, N.; Sprekeler, H.; Gerstner, W. (2010). "Functional requirements for reward-modulated spike-timing-dependent plasticity". Journal of Neuroscience. 30 (40): 13326–13337. doi:10.1523/JNEUROSCI.6249-09.2010. PMC 6634722. PMID 20926659.
  • Vogels, T. P.; Sprekeler, H.; Zenke, F.; Clopath, C.; Gerstner, W. (2011). "Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks". Science. 334 (6062): 1569–1573. Bibcode:2011Sci...334.1569V. doi:10.1126/science.1211095. PMID 22075724.
  • Sprekeler, H. (2011). "On the relation of slow feature analysis and Laplacian eigenmaps". Neural Computation. 23 (12): 3287–3302. doi:10.1162/NECO_a_00214. PMID 21919780.
  • Naud, R.; Sprekeler, H. (2018). "Sparse bursts optimize information transmission in a multiplexed neural code". Proceedings of the National Academy of Sciences. 115 (27): E6329–E6338. Bibcode:2018PNAS..115E6329N. doi:10.1073/pnas.1720995115. PMC 6142200. PMID 29934400.

References

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  1. 1 2 3 4 5 "Henning Sprekeler accepts professorship at TU Berlin and Bernstein Center Berlin". Bernstein Coordination Site, via idw-online. 2014. Retrieved 12 September 2026.
  2. ↑ "Modeling of Cognitive Processes – Principal Investigator". Technische Universität Berlin. Retrieved 12 September 2026.
  3. 1 2 "vogels_sprekeler_synapse – Synapse type for symmetric spike-timing dependent plasticity with constant depression". NEST Simulator documentation, NEST Initiative. Retrieved 12 September 2026.
  4. 1 2 "Balanced network with inhibitory plasticity – Vogels et al. 2011". Open Source Brain. Retrieved 12 September 2026.
  5. 1 2 3 4 "1,25 Millionen Euro für Hirnforscher Henning Sprekeler" (in German). WISTA Management GmbH. 6 October 2011. Retrieved 12 September 2026.
  6. ↑ Sprekeler, H.; Kießlich, G.; Wacker, A.; Schöll, E. (2004). "Coulomb effects in tunneling through a quantum dot stack". Physical Review B. 69 (12) 125328. arXiv:cond-mat/0309696. Bibcode:2004PhRvB..69l5328S. doi:10.1103/PhysRevB.69.125328.
  7. ↑ "Henning Sprekeler". Bernstein Network Computational Neuroscience. Retrieved 12 September 2026.
  8. ↑ "Henning Sprekeler". Sprekeler Lab, Technische Universität Berlin. Retrieved 12 September 2026.
  9. ↑ "Henning Sprekeler". Cluster of Excellence Science of Intelligence. Retrieved 12 September 2026.
  10. ↑ "Prof. Dr. Henning Sprekeler". Collaborative Research Centre 1315. Retrieved 12 September 2026.
  11. ↑ Wiskott, Laurenz; Berkes, Pietro; Franzius, Mathias; Sprekeler, Henning; Wilbert, Niko (2011). "Slow feature analysis". Scholarpedia. 6 (4): 5282. Bibcode:2011SchpJ...6.5282W. doi:10.4249/scholarpedia.5282.
  12. ↑ Sprekeler, H. (2011). "On the relation of slow feature analysis and Laplacian eigenmaps". Neural Computation. 23 (12): 3287–3302. doi:10.1162/NECO_a_00214. PMID 21919780.
  13. ↑ Sprekeler, H.; Zito, T.; Wiskott, L. (2014). "An extension of slow feature analysis for nonlinear blind source separation". Journal of Machine Learning Research. 15: 921–947.
  14. ↑ Sprekeler, H.; Michaelis, C.; Wiskott, L. (2007). "Slowness: an objective for spike-timing-dependent plasticity?". PLOS Computational Biology. 3 (6) e112. Bibcode:2007PLSCB...3..112S. doi:10.1371/journal.pcbi.0030112. PMC 1904380. PMID 17604445.
  15. ↑ Franzius, M.; Sprekeler, H.; Wiskott, L. (2007). "Slowness and sparseness lead to place, head-direction, and spatial-view cells". PLOS Computational Biology. 3 (8) e166. Bibcode:2007PLSCB...3..166F. doi:10.1371/journal.pcbi.0030166. PMC 1963505. PMID 17784780.
  16. 1 2 Froemke, R. C. (2015). "Plasticity of cortical excitatory-inhibitory balance". Annual Review of Neuroscience. 38: 195–219. doi:10.1146/annurev-neuro-071714-034002. PMC 4652600. PMID 25897875.
  17. ↑ Vogels, T. P.; Sprekeler, H.; Zenke, F.; Clopath, C.; Gerstner, W. (2011). "Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks". Science. 334 (6062): 1569–1573. Bibcode:2011Sci...334.1569V. doi:10.1126/science.1211095. PMID 22075724.
  18. ↑ "Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011), model 143751". ModelDB, Yale University. Retrieved 12 September 2026.
  19. ↑ Hennequin, G.; Agnes, E. J.; Vogels, T. P. (2017). "Inhibitory plasticity: balance, control, and codependence". Annual Review of Neuroscience. 40: 557–579. doi:10.1146/annurev-neuro-072116-031005. PMID 28598717.
  20. ↑ Sprekeler, H. (2017). "Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond". Current Opinion in Neurobiology. 43: 198–203. doi:10.1016/j.conb.2017.03.014. PMID 28500933.
  21. ↑ Frémaux, N.; Sprekeler, H.; Gerstner, W. (2010). "Functional requirements for reward-modulated spike-timing-dependent plasticity". Journal of Neuroscience. 30 (40): 13326–13337. doi:10.1523/JNEUROSCI.6249-09.2010. PMC 6634722. PMID 20926659.
  22. ↑ Schroeder, A.; et al. (2023). "Inhibitory top-down projections from zona incerta mediate neocortical memory". Neuron. 111 (5): 727–738. doi:10.1016/j.neuron.2022.12.010. PMID 36610397.
  23. ↑ Jolles, J. W.; et al. (2023). "Fish shoals resemble a stochastic excitable system driven by environmental perturbations". Nature Physics. 19 (5): 1146–1151. Bibcode:2023NatPh..19..663G. doi:10.1038/s41567-022-01916-1.
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Category:Living people Category:German neuroscientists Category:Computational neuroscientists Category:Academic staff of the Technical University of Berlin Category:Humboldt University of Berlin alumni Category:University of Freiburg alumni Category:Academics of the University of Cambridge Category:Year of birth missing (living people)

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