Inhalt des Dokuments
Prof. Dr. Henning Sprekeler
Technische Universität Berlin & Bernstein Center for Computational Neuroscience Berlin
Fak. IV - Elektrotechnik und Informatik
Modelling of Cognitive Processes
Kontakt:
Raum MAR 5.009
Tel.: +49 30 314 24390
Office hours (with appointment):
Tuesdays, 2.15 to 3.00 p.m.
Research Interests
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.
Awards
- Bernstein Award for Computational Neuroscience 2011, German Federal Ministry of Education and Research
- Humboldt-Award for Outstanding Dissertation 2008, Humboldt-University Berlin
Research Projects
- 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)
Teaching
Course | hours/week | ECTS |
---|---|---|
Cognitive Neuroscience | 2 | 2 |
Theoretical Lecture | 2 | 2 |
Analytical Tutorial | 2 | 4 |
Programming Tutorial | 2 | 4 |
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/
Course | hours/week | ECTS |
---|---|---|
Seminar | 2 | 3 |
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
Course | Stunden/Woche | ECTS |
---|---|---|
Vorlesung mit Übung | 4 | 6 |
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
Publications
Preprints
- 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
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 - 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 Comput Biol 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)
Zusatzinformationen / Extras
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Cathrin BunkelmannModelling of Cognitive Processes
Building MAR
Room 5011
030 - 314 73557
cognition@tu-berlin.de
Tue - Thur 9.00-15.00