Matthias H. Hennig, PhD
Affiliations: | Institute for Adaptive and Neural Computation | University of Edinburgh, Edinburgh, Scotland, United Kingdom |
Area:
computational neuroscience, retina, neural development, homeostasis, synaptic transmissionWebsite:
http://homepages.inf.ed.ac.uk/mhennig/Google:
"Matthias Hennig"Mean distance: 14.42 (cluster 17) | S | N | B | C | P |
Parents
Sign in to add mentor| Florentin Wörgötter | grad student | 2000-2004 | |
| Bruce Graham | post-doc | 2005-2005 | University of Stirling |
| David Willshaw | post-doc | 2006-2008 | Edinburgh |
Children
Sign in to add trainee| Yann Sweeney | grad student | 2011-2015 | Edinburgh |
| Martino Sorbaro | grad student | 2014-2018 | Edinburgh |
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Publications
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| Turishcheva P, Fahey PG, Vystrčilová M, et al. (2024) Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos. Arxiv |
| Pamplona D, Hilgen G, Hennig MH, et al. (2022) Receptive field estimation in large visual neuron assemblies using a super-resolution approach. Journal of Neurophysiology. 127: 1334-1347 |
| Scholl C, Rule ME, Hennig MH. (2021) The information theory of developmental pruning: Optimizing global network architectures using local synaptic rules. Plos Computational Biology. 17: e1009458 |
| Hurwitz C, Kudryashova N, Onken A, et al. (2021) Building population models for large-scale neural recordings: Opportunities and pitfalls. Current Opinion in Neurobiology. 70: 64-73 |
| Rule ME, Sorbaro M, Hennig MH. (2020) Optimal Encoding in Stochastic Latent-Variable Models. Entropy (Basel, Switzerland). 22 |
| Rule ME, Sorbaro M, Hennig MH. (2020) Optimal Encoding in Stochastic Latent-Variable Models Entropy. 22: 714 |
| Rule ME, Schnoerr D, Hennig MH, et al. (2019) Neural field models for latent state inference: Application to large-scale neuronal recordings. Plos Computational Biology. 15: e1007442 |
| Rajaram E, Kaltenbach C, Fischl MJ, et al. (2019) Slow NMDA-mediated excitation accelerates offset-response latencies generated via a post-inhibitory rebound mechanism. Eneuro |
| Hennig MH, Hurwitz C, Sorbaro M. (2019) Scaling Spike Detection and Sorting for Next-Generation Electrophysiology. Advances in Neurobiology. 22: 171-184 |
| Jouty J, Hilgen G, Sernagor E, et al. (2018) Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina. Frontiers in Cellular Neuroscience. 12: 481 |