Prof. Dr. Henning Sprekeler

Technische Universität Berlin & Bernstein Center for Computational Neuroscience Berlin [2]

Fak. IV - Elektrotechnik und Informatik

Modellierung Kognitiver Prozesse

Kontakt:

Raum MAR 5.009

Tel.: +49 30 314 24390

h.sprekeler@tu-berlin.de [3]

Sprechstunde (nach Anmeldung):
Dienstag, 14.15 -15.00 Uhr

We investigate the neuronal basis of cognitive abilities such as perception, learning and memory, and decision making. To this end, we use mathematical and computational methods to bridge gaps between the microscopic level of synapses, neurons and neuronal networks and the cognitive level. A particular focus lies on how changes on the neuronal level — for example synaptic plasticity — allow our brain to dynamically adjust to environmental requirements.

• Bernstein Award for Computational Neuroscience 2011, German Federal Ministry of Education and Research
• Humboldt-Award for Outstanding Dissertation 2008, Humboldt-University Berlin

• The functional and computational role of various types of interneurons in a neural network. (Together with Loreen Hertäg)
• The interaction of global reward signals with local learning rules and their impacts on cognition. (Together with David Higgins)
• How different kinds of inhibitory plasticity affect dynamics and information processing in recurrent networks. (Together with Owen Mackwood)
• Computational models of presynaptic inhibition. (Together with Laura Naumann)
• Interplay between action and perception in reinforcement learning agents. (Together with Mathias Schmerling)
• The consequences of long-range top down connections on local network dynamics including 1) dendritic processes, 2) interneuron circuits and 3) synaptic mechanisms. (Together with Filip Vercruysse)
• The effects of inhibitory plasticity on adaptive sensory and spatial processing. (Together with Simon Weber)

Offered each summer term.

This module is compulsory for students enrolled in the Master program Computational Neuroscience.
Module components are compulsory elective or elective for students of other Master and Diploma programs of Berlin’s universities, who wish to specialize in the Cognitive Neurosciences.

See also: www.bccn-berlin.de/Graduate+Programs/0_Teaching/Courses+and+Modules/ [4]

Offered in both summer and winter term.

This module is targeted at master students and researchers in the field of computational neuroscience. Mathematical skills and a basic familiarity with neuroscientific concepts are an advantage. 

Please enroll in the moodle: Link [5]

Dieser Kurs wird jedes Semester angeboten.

Er richtet sich primär an Bachelor- Studenten der Wirschaftsinformatik und Technischen Informatik.

Hier finden Sie den ISIS-Link: Link [6]

Preprints

• R. Naud, H. Sprekeler
  Burst Ensemble Multiplexing: A Neural Code Connecting Dendritic Spikes with Microcircuits
  bioRxiv, doi.org/10.1101/143636
  pdf [7]
• S.N. Weber, H. Sprekeler
  Learning place cells, grid cells and invariances: A unifying model 
  bioRxiv, doi.org/10.1101/102525 [8]
  pdf [9]
• 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 [10]
  pdf [11]

Publications

• A. Kutschireiter, S.C.  Surace, H.  Sprekeler, J.P.  Pfister (2017)
  Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception
  Scientific Reports 7, Article number: 8722 (2017), doi:10.1038/s41598-017-06519-y
  pdf [12]
• H. Sprekeler (2017)
  Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond
  Current Opinion in Neurobiology 43, 198-203
  pdf [13]
• N. Chenkov, H. Sprekeler, R. Kempter (2017)
  Memory replay in balanced recurrent networks
  PLoS Comput Biol 13(1): e1005359
  pdf [14]
• 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 [15]
• 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 [16]
• 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 [17]
• 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 [18]
• 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 [19]
• 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 [20]
• W. Gerstner, H. Sprekeler, G. Deco (2012)
  Theory and simulation in neuroscience
  Science 338:60-65
  pdf [21] 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 [22]
• 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 [23] 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 [24]
• H. Sprekeler (2011)
  On the Relation of Slow Feature Analysis and Laplacian Eigenmaps
  Neural Computation 23:3287-3302
  pdf [25]
• 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 [26]
• N. Fremaux*, H. Sprekeler* and W. Gerstner (2010)
  Functional Requirements for Reward-modulated Spike Timing-Dependent Plasticity
  Journal of Neuroscience 30:13326-13337
  pdf [27]
• L. Wiskott, P. Berkes, M. Franzius, H. Sprekeler and N. Wilbert (2010)
  Slow Feature Analysis
  Scholarpedia, 6(4):5282
  link [28]
• 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 [29] 
• F. Creutzig and H. Sprekeler (2008)
  Predictive Coding and the Slowness Principle: An Information-Theoretic Approach
  Neural Computation 20:1026-41
  pdf [30]
• 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 [31]
• H. Sprekeler, C. Michaelis and L. Wiskott (2007)
  Slowness: An Objective for Spike-Timing-Dependent Plasticity?
  PLoS Computational Biology 3(6):e112
  pdf [32]
• 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) [33]
• 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) [34]