Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data.

ABSTRACT: Computational 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, unbia

SUBMITTER: Gutzen R 

PROVIDER: S-EPMC6305903 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature