Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models.

ABSTRACT: Any modeler who has attempted to reproduce a spiking neural network model from its description in a paper has discovered what a painful endeavor this is. Even when all parameters appear to have been specified, which is rare, typically the initial attempt to reproduce the network does not yield results that are recognizably akin to those in the original publication. Causes include inaccurately reported or hidden parameters (e.g., wrong unit or the existence of an initialization distribution), differences in implementation of model dynamics, and ambiguities in the text description of the network experiment. The very fact that adequate reproduction often cannot be achieved until a series of such causes have been tracked down and resolved is in itself disconcerting, as it reveals unreported mo

SUBMITTER: Pauli R 

PROVIDER: S-EPMC6085985 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature