Henning Sprekeler
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Henning Sprekeler | |
|---|---|
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| Born | Germany |
| Known for | Inhibitory Neuroplasticity, Information bottleneck method |
| Awards | Bernstein Award in Computational Neuroscience |
| Academic background | |
| Alma mater | Technical University of Berlin, Humboldt University of Berlin |
| Doctoral advisor | Prof. Dr. Laurenz Wiskott |
| Academic work | |
| Discipline | Theoretical Neuroscience |
| Institutions | Humboldt University of Berlin |
| Main interests | Neural mechanisms of Self-organization; synaptic plasticity; functional role of inhibition |
Henning Sprekeler (born 1975) is a German neuroscientist who is a Professor of Theoretical Computer Science at the Technical University Berlin. He studies synaptic plasticity and its effects on network dynamics and behavior. He is particularly interested in the functional role of neuronal inhibition and neural methods of Self-organization.
Education and early research
Sprekeler was born in Münster, Westphalia, where he also graduated with a German High School degree from the Immanuel-Kant-Gymnasium, Münster-Hiltrup in 1996. He started his academic career in Physics at the University of Freiburg in Freiburg in 1997. In 1999 he switched to the Technical University Berlin where he obtained his Diploma in Physics in 2003. After graduating, Sprekeler joined the group of Prof. Dr. L. Wiskott at the Institute of Theoretical Biology of the Humboldt University (HU) Berlin. Here, he established a mathematical framework of Slow Feature Analysis [1] and used the slowness principle to explain the emergence of spatial memory cells. He earned his Ph.D. in Theoretical Biology in 2008.
Scientific career
Henning Sprekeler currently holds a full professorship (W3) in the Department of Electrical Engineering and Computer Science at the Technical University Berlin. Sprekeler is interested in the mechanisms of intelligent behavior in artificial systems and the brain, with a particular focus on self-organization and learning [2]. One major area of focus within Prof. Sprekeler’s research group centers on studying the function and adaptability of neural circuits in the brain. The group investigates how these circuits self-organize, process information and undergo changes over time. In particular, they explore the intricate dynamics of inhibitory circuits, using computational network models that bridge experimental observations at the neural or microcircuit level to a functional or systems level. Together with Wulfram Gerstner, Tim Vogels, and Claudia Clopath he published highly influential [3] research on the role of inhibitory mechanisms of neuroplasticity in shaping balanced excitation and inhibition across cortical circuits. Another significant line of Sprekeler’s research lies in using computational models to investigate animal behavior. Collaborating with experimental partners from the Excellence Cluster Science of Intelligence [4], his research group analyzes data from behavioral experiments of mice and fish to study collective intelligence.
Awards and fellowships
Henning Sprekeler has received recognition for his academic achievements and contributions to the field of cognitive process modeling. In 2011 the German Ministry for Science and Education awarded Henning Sprekeler the Bernstein Award for Computational Neuroscience[5] in 2011. Additionally, Sprekeler received the Humboldt-Award for his distinguished Ph.D. thesis in 2008. More recently Sprekeler has also been recognised for his outstanding online teaching during the COVID-19 pandemic.
Select publications
- Sprekeler, H.; Vogels, T. P.; Zenke, F.; Clopath, C.; Gerstner, W. (2011). "Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks". Science. 334 (1569–1573). doi:10.1126/science.1211095. PMC 3341569. PMID 22174250.
- Frémaux, N.; Sprekeler, H.; Gerstner, W. (2010). "Functional requirements for reward-modulated spike-timing-dependent plasticity". J Neurosci. 30 (40): 13326–13337. doi:10.1523/JNEUROSCI.6249-09.2010. PMC 2968885. PMID 20926676.
- Franzius, M.; Sprekeler, H.; Wiskott, L. (2007). "Slowness and sparseness lead to place, head-direction and spatial-view cells". PLoS Comp Biol. 3 (8): e166. doi:10.1371/journal.pcbi.0030166. PMC 1942344. PMID 17713996.
- Sprekeler, H.; Michaelis, C.; Wiskott, L. (2007). "Slowness: An Objective for spike timing-dependent plasticity?". PLoS Comp Biol. 3 (6): e112. doi:10.1371/journal.pcbi.0030112. PMC 1877856. PMID 17530927.
- Frémaux, N.; Sprekeler, H.; Gerstner, W. (2013). "Reinforcement learning using a continuous time actor-critic framework with spiking neurons". PLoS Comput Biol. 9 (4): e1003024. doi:10.1371/journal.pcbi.1003024. PMC 3630069. PMID 23633959.
- 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. doi:10.1073/pnas.1802494115. PMC 6142215. PMID 29941589.
- Mackwood, O.; Naumann, L. B.; Sprekeler, H. (2021). "Learning excitatory-inhibitory neuronal assemblies in recurrent networks". Elife. 10: e59715. doi:10.7554/eLife.59715. PMC 8205131. PMID 34053121.
- Hertäg, L.; Sprekeler, H. (2020). "Learning prediction error neurons in a canonical interneuron circuit". Elife. 9: e57541. doi:10.7554/eLife.57541. PMC 7133709. PMID 32223894.
- Weber, S. N.; Sprekeler, H. (2018). "Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity". Elife. 7: e34560. doi:10.7554/eLife.34560. PMC 5823711. PMID 29485745.
- Vischer, M. A.; Lange, R. T.; Sprekeler, H. (2022). "On lottery tickets and minimal task representations in deep reinforcement learning". ICLR.
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References
External links
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