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000858539 0247_ $$2doi$$a10.3389/fninf.2018.00090
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000858539 1001_ $$0P:(DE-Juel1)171572$$aGutzen, Robin$$b0$$eCorresponding author
000858539 245__ $$aReproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data
000858539 260__ $$aLausanne$$bFrontiers Research Foundation$$c2018
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000858539 520__ $$aComputational neuroscience relies on simulations of neural network models to bridge the gap between the theory of neural networks and the experimentally observed activity dynamics in the brain. The rigorous validation of simulation results against reference data is thus an indispensable part of any simulation workflow. Moreover, the availability of different simulation environments and levels of model description require also validation of model implementations against each other to evaluate their equivalence. Despite rapid advances in the formalized description of models, data, and analysis workflows, there is no accepted consensus regarding the terminology and practical implementation of validation workflows in the context of neural simulations. This situation prevents the generic, unbiased comparison between published models, which is a key element of enhancing reproducibility of computational research in neuroscience. In this study, we argue for the establishment of standardized statistical test metrics that enable the quantitative validation of network models on the level of the population dynamics. Despite the importance of validating the elementary components of a simulation, such as single cell dynamics, building networks from validated building blocks does not entail the validity of the simulation on the network scale. Therefore, we introduce a corresponding set of validation tests and present an example workflow that practically demonstrates the iterative model validation of a spiking neural network model against its reproduction on the SpiNNaker neuromorphic hardware system. We formally implement the workflow using a generic Python library that we introduce for validation tests on neural network activity data. Together with the companion study (Trensch et al., sub.), the work presents a consistent definition, formalization, and implementation of the verification and validation process for neural network simulations.
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000858539 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x2
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000858539 7001_ $$0P:(DE-Juel1)171972$$avon Papen, Michael$$b1
000858539 7001_ $$0P:(DE-Juel1)168379$$aTrensch, Guido$$b2
000858539 7001_ $$0P:(DE-Juel1)164108$$aQuaglio, Pietro$$b3
000858539 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b4
000858539 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b5
000858539 773__ $$0PERI:(DE-600)2452979-5$$a10.3389/fninf.2018.00090$$gVol. 12, p. 90$$p90$$tFrontiers in neuroinformatics$$v12$$x1662-5196$$y2018
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