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Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models
Robin Pauli1, Philipp Weidel1, Susanne Kunkel2,3
1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany.
Frontiers in Neuroinformatics
|August 21, 2018
Summary
Reproducing spiking neural network (SNN) models is challenging due to hidden parameters and implementation details. We propose best practices to improve the reproducibility and scientific quality of SNN research.
Area of Science:
- Computational neuroscience
- Systems neuroscience
- Computational modeling
Background:
- Reproducing spiking neural network (SNN) models from publications is often difficult.
- Inaccurately reported parameters, implementation differences, and ambiguous descriptions hinder replication.
Purpose of the Study:
- To demonstrate the challenges in reproducing SNN models, even with provided source code.
- To derive a guideline of best practices for enhancing the reproducibility of SNN research.
Main Methods:
- A case study of reproducing a seminal SNN model was conducted.
- Analysis focused on identifying sensitivities to implementation choices (e.g., noise, timestep, analysis parameters).
Main Results:
- Reproducing the SNN model was time-consuming and frustrating, despite the availability of source code.
- The reproduced model exhibited high sensitivity to specific implementation details.
Conclusions:
- Unreported dependencies on implementation choices diminish model credibility.
- A guideline of best practices is proposed to improve the reproducibility and scientific rigor of SNN models.
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