AG Neural Data Science
Data recorded in modern neuroscience is increasingly complex and high dimensional. To face this challenge, the Neural Data Science group pursues two main lines of investigation.
- Behavior correlates in neuronal population activity
The first line focuses on using state of the art mathematical and computational methods to study the activity of neuronal networks as well as animal behavior. This involves finding the right models and techniques to interpret noisy, high-throughput neural data, such as in-vivo electrophysiological and calcium imaging recordings.
Specific aims include:
- Understanding low-dimensional dynamics underlying population activity in healthy animals and disease models
- Understanding the role of behavioral correlations emerging during unconstrained behavior as well as learning-related tasks
- Understanding the complexity of single neuron input-output computations
- Computational methods
The second line of research focuses on building new types of computational tools and machine learning models to answer the questions discussed above. To this end, the group aims to develop novel methods for measurement and analysis of animal behavior, as well as suitable statistical methods for correlating behavioral patterns to neural activity. For this purpose, the group combines a wide range of computational methods including signal processing, computer vision, as well as statistical machine learning.
Specific developments include:
- Probabilistic methods for extraction of low-dimensional structures from high dimensional data
- Unsupervised machine learning approaches for behavior quantification
- Machine vision algorithms for behavior tracking in complex environments
Pavol Bauer studied Medical Informatics at the Vienna University of Technology and received his doctorate in Scientific Computing from the Uppsala University in Sweden. He thereafter held a postdoctoral position at the laboratory of Stefan Remy at the German Center for Neurodegenerative Diseases in Bonn pursuing research on the correlation of neuronal activity and behavioral patterns of freely moving mice. Since January 2020 he is head of the Neural Data Analysis group at the Department of Cellular Neuroscience.
Sarkar I, Maji I, Omprakash C, Stober S, Mikulovic S, Bauer P. 2021. Evaluation of deep lift pose models for 3D rodent pose estimation based on geometrically triangulated data. CV4Animals Workshop, CVPR 2021. https://arxiv.org/abs/2106.12993
Wachtler T, Bauer P, Denker M, Grün S, Hanke M, Klein J, Oeltze-Jafra S, Ritter P, Rotter S, Scherberger H, Stein A, Witte OW. NFDI-Neuro: Building a community for neuroscience research data management in Germany. Neuroforum (2020). doi:10.1515/nf-2020-0036
Luxem K, Fuhrmann F, Kürsch J, Remy S, Bauer P. Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion. BioRxiv (2020). doi:10.1101/2020.05.14.095430
Widgren S, Bauer P, Eriksson R, Engblom S. SimInf: A Package for Data-Driven Stochastic Disease Spread Simulations. J Stat Soft. 2019;91(12). doi:10.18637/jss.v091.i12
Lindén J, Bauer P, Engblom S, Jonsson B. Exposing Inter-process Information for Efficient PDES of Spatial Stochastic Systems on Multicores. ACM Trans Model Comput Simul. 2019;29(2):1-25. doi:10.1145/3301500
Luxem K, Fuhrmann F, Remy S, Bauer P. Hierarchical network analysis of behavior and neuronal population activity. In: 2019 Conference on Cognitive Computational Neuroscience. Berlin, Germany: Cognitive Computational Neuroscience; 2019. doi:10.32470/CCN.2019.1261-0
Lindén J, Bauer P, Engblom S, Jonsson B. Fine-Grained Local Dynamic Load Balancing in PDES. In: Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation - SIGSIM-PADS ’18. Rome, Italy: ACM Press; 2018:201-212. doi:10.1145/3200921.3200928
Bauer P, Engblom S, Mikulovic S, Senek A. Multiscale modelling via split-step methods in neural firing. Mathematical and Computer Modelling of Dynamical Systems. 2018;24(4):426-445. doi:10.1080/13873954.2018.1488740
Mikulovic S, Restrepo CE, Siwani S, et al. Ventral hippocampal OLM cells control type 2 theta oscillations and response to predator odor. Nat Commun. 2018;9(1):3638. doi:10.1038/s41467-018-05907-w
Bauer P, Engblom S, Widgren S. Fast event-based epidemiological simulations on national scales. The International Journal of High Performance Computing Applications. 2016;30(4):438-453. doi:10.1177/1094342016635723
Widgren S, Engblom S, Bauer P, Frössling J, Emanuelson U, Lindberg A. Data-driven network modelling of disease transmission using complete population movement data: spread of VTEC O157 in Swedish cattle. Vet Res. 2016;47(1):81. doi:10.1186/s13567-016-0366-5
Bauer P, Lindén J, Engblom S, Jonsson B. Efficient Inter-Process Synchronization for Parallel Discrete Event Simulation on Multicores. In: Proceedings of the 3rd ACM Conference on SIGSIM-Principles of Advanced Discrete Simulation - SIGSIM-PADS ’15. London, United Kingdom: ACM Press; 2015:183-194. doi:10.1145/2769458.2769476
Patra K, Lyons DJ, Bauer P, et al. A role for solute carrier family 10 member 4, or vesicular aminergic-associated transporter, in structural remodelling and transmitter release at the mouse neuromuscular junction. Eur J Neurosci. 2015;41(3):316-327. doi:10.1111/ejn.12790
Milias-Argeitis A, Engblom S, Bauer P, Khammash M. Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks. J R Soc Interface. 2015;12(113):20150831. doi:10.1098/rsif.2015.0831
Bauer P, Engblom S. Sensitivity Estimation and Inverse Problems in Spatial Stochastic Models of Chemical Kinetics. Lecture Notes in Computational Science and Engineering; 2015:519-527. doi:10.1007/978-3-319-10705-9_51