Advancing processes for verification and validation in neuroscience

^{Copyright: Formalisation of model verification and validation processes (left) as implemented in the framework of the SciUnit-based NetworkUnit validation library. Figures from Gutzen et al. (2018).}

As the complexity of research topics in neuroscience grows, so does the urgency for rigorous methods of verification (does a computational process produce the correct result?) and validation (does a model represent features of the system of interest?). In the context of tools for data analysis, the team considers reproducibility of prior scientific findings as a major element of verification and validation processes (Rostami et al., 2017). With regard to model validation, the team is engaged with conceptual work as well as concrete implementations of software (NetworkUnit, https://github.com/INM-6/NetworkUnit) to measure the degree of agreement between neuronal network simulations and experimental data (Trensch et al., 2018; Gutzen et al., 2018). To build valid validation processes that exploit the richness of data types obtained across experimental techniques and that make use of the diversity of analysis approaches, the team investigates the design of multi-modal, multi-methodological, interoperable workflows (see video here).

Publications for this project are:

• Gutzen R., von Papen M., Trensch G., Quaglio P., Grün S., Denker M. (2018) Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. Frontiers in Neuroinformatics 12, 90.
  DOI: 10.3389/fninf.2018.00090
  
• Rostami V., Ito J., Denker M., Grün S. (2017) [Re] Spike Synchronization And Rate Modulation Differentially Involved In Motor Cortical Function. ReScience 3, 3.
  DOI: 10.5281/zenodo.583814
  
  
• Trensch G., Gutzen R., Blundell I., Denker M., Morrison A. (2018) Rigorous Neural Network Simulations: A Model Substantiation Methodology for Increasing the Correctness of Simulation Results in the Absence of Experimental Validation Data. Frontiers in Neuroinformatics 12, 81.
  DOI: 10.3389/fninf.2018.00081