Team Data Science of Electro- and Optophysiology in Behavioural Neuroscience
Computational neuroscience is focused on gaining an understanding of the principles of information processing in the brain. This involves the development of theories and models of brain function, as well as the design of analysis methods to probe for signatures of the corresponding dynamics in data from the brain. Models and experimental data cover various spatio-temporal scales, are represented by multiple modalities of observation, and address a diversity of neuroscientific concepts expressed on varying levels of abstraction. At the same time, neuroscientists have a diversified and constantly growing repertoire of methodologies to analyze their data, at the cost of higher complexity and computational expense. The team Data Science for Electro- and Optophysiology Behavioural Neuroscience addresses the challenges that arise from this situation in the context of data management and reproducible data analytics in neuroscience with concepts from physics and computer science. To this end, the team develops and maintains practical software solutions covering data acquisition, management, and analysis of electrophysiological data from experiment and simulation, and formalized approaches model validation. These solutions form the basis for neuroscience research conducted in the team that analyzes spatio-temporal brain activity observed across spatio-temporal scales and measurement modalities.
neuroscience, reproducible science, data analytics, electrophysiology, optophysiology, data management, simulation science
Dr. Michael Denker
Building 15.22 / Room 4009
In the past decades, neuroscientists have witnessed a rapid increase in the complexity and volume of data, including, in particular, the subdomains of electrophysiology and optophysiology. Today, electro- and optophysiological experiments combine recordings of hundreds of channels in parallel with behaviour under increasingly natural conditions. Minute details of the experiment may become relevant for the analysis of such rich data, often in ways that are hardly foreseen during the time when the experiment is conducted. Despite the importance of unambiguous and machine-readable metadata describing the experiments, current workflows for acquisition and post-processing are largely homegrown, use custom data and metadata representations, are difficult to re-use, and rely on error-prone manual intervention. The team addresses the need for defining practical solutions to automate, standardise, and streamline data and metadata acquisition in the experimental laboratory (Zehl et al., 2016). This process leads to the identification and implementation of missing software components (Sprenger et al., 2019) for digitised acquisition workflows that assist scientists in creating high-quality publications of their precious data (Brochier et al, 2018). To expedite bringing concepts of FAIR data management to wide-spread use in the laboratories, the team is among the initial members to spark the NFDI Neuroscience consortium (http://nfdi-neuro.de) as a community for exchange of knowledge on research data management.
Publications for this project are:
- Brochier T., Zehl L., Hao Y., Duret M., Sprenger J., Denker M., Grün S., Riehle A. (2018) Massively Parallel Recordings in Macaque Motor Cortex during an Instructed Delayed Reach-to-Grasp Task. Scientific Data 5, 180055.
- Sprenger J., Zehl L., Pick J., Sonntag M., Grewe J., Wachtler T., Grün S., Denker M. (2019) odMLtables: A User-Friendly Approach for Managing Metadata of Neurophysiological Experiments. Front. Neuroinform. 13, 62.
- Zehl L., Jaillet F., Stoewer A., Grewe J., Sobolev A., Wachtler T., Brochier TG., Riehle A., Denker M., Grün S. (2016) Handling Metadata in a Neurophysiology Laboratory. Frontiers in Neuroinformatics 10, 26.
Designing workflows for reproducible data acquisition and post-processing. Figure modified from: Zehl et al. (2016).