A long journey into reproducible computational neuroscience

Computational neuroscience is a powerful ally in our quest to understand the brain. Even the most simple model can shed light on the role of this or that structure and propose new hypothesis concerning the overall brain organization. However, any model in Science is doomed to be proved wrong or incomplete and replaced by a more accurate one. In the meantime, for such replacement to happen, we have first to make sure that models are actually reproducible such that they can be tested, evaluated, criticized and ultimately modified, replaced or even rejected. This is where the shoe pinches. If we cannot reproduce a model in the first place, we're doomed to re-invent the wheel again and again, preventing us from building an incremental computational knowledge of the brain.

• Publication year

  2015

• Venue

  Frontiers in Computational Neuroscience

• Publication date

  2015-03-05

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.3389/fncom.2015.00030PMID 25798104PMCID 4350388
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Programming: Pick up Python

  2015cited by this paper
• Better Software, Better Research

  2014cited by this paper
• Interactive notebooks: Sharing the code

  2014cited by this paper
• Ten Simple Rules for Effective Computational Research

  2014cited by this paper
• Implementing Reproducible Research

  2014cited by this paper
• Ten Simple Rules for Reproducible Computational Research

  2013cited by this paper
• Interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study.

  2013cited by this paper
• DANA: Distributed numerical and adaptive modelling framework

  2012cited by this paper
• Best Practices for Scientific Computing

  2012cited by this paper
• Making neurophysiological data analysis reproducible: why and how?

  2012cited by this paper
• Reproducible Research in Computational Science

  2011cited by this paper
• An invitation to reproducible computational research.

  2010cited by this paper
• How do scientists develop and use scientific software?

  2009cited by this paper
• Towards Reproducible Descriptions of Neuronal Network Models

  2009cited by this paper
• Brian: A Simulator for Spiking Neural Networks in Python

  2008cited by this paper
• Where neuroscience and dynamic system theory meet autonomous robotics: A contracting basal ganglia model for action selection

  2008cited by this paper
• IPython: A System for Interactive Scientific Computing

  2007cited by this paper
• Competition between Feedback Loops Underlies Normal and Pathological Dynamics in the Basal Ganglia

  2006cited by this paper
• A computational model of action selection in the basal ganglia. I. A new functional anatomy

  2001cited by this paper
• Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain

  2000cited by this paper
• Reinforcement Learning: An Introduction

  1998cited by this paper
• WaveLab and Reproducible Research

  1995cited by this paper
• Empirical Model‐Building and Response Surfaces

  1988influential reference

• Reward-driven adaptation of movements requires strong recurrent basal ganglia-cortical loops

  2025cites this paper
• Reproducibility, Replicability, and Repeatability: A survey of reproducible research with a focus on high performance computing

  2024cites this paper
• PyRates -- A Code-Generation Tool for Dynamical Systems Modeling

  2023cites this paper
• PyRates—A code-generation tool for modeling dynamical systems in biology and beyond

  2023cites this paper
• NIT: an open-source tool for information theoretic analysis of neural population data

  2022cites this paper
• Reproducing a decision-making network in a virtual visual discrimination task

  2022cites this paper
• An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls

  2021cites this paper
• A Parametric Framework to Generate Visual Illusions Using Python

  2021cites this paper
• Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

  2021cites this paper
• The impact of mathematical modeling languages on model quality in systems biology: A software engineering perspective

  2020cites this paper
• The State of Reproducible Research in Computer Science

  2020cites this paper
• Reproducibility and Rigour in Computational Neuroscience

  2020cites this paper
• CG‐SENSE revisited: Results from the first ISMRM reproducibility challenge

  2020cites this paper
• NeuroKit2: A Python toolbox for neurophysiological signal processing

  2020cites this paper
• Bayesian encoding and decoding as distinct perspectives on neural coding

  2020cites this paper
• Required characteristics for modeling languages in systems biology: A software engineering perspective

  2019cites this paper
• Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices.

  2019cites this paper
• Computational Models of Astrocytes and Astrocyte–Neuron Interactions: Characterization, Reproducibility, and Future Perspectives

  2019cites this paper
• Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective

  2019cites this paper
• A natural history of skills

  2018cites this paper
• Terminologies for Reproducible Research

  2018cites this paper
• Computational Models for Calcium-Mediated Astrocyte Functions

  2018cites this paper
• Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

  2018cites this paper
• Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models

  2018cites this paper
• Performing reproductions to understand the state of reproducibility in current AI research

  2018cites this paper
• Towards a discipline of performance engineering : lessons learned from stencil kernel benchmarks

  2018cites this paper
• The Newsletter of the Technical Committee on Cognitive and Developmental Systems Computational Modelling across Disciplines

  2017cites this paper
• Sustainable computational science: the ReScience initiative

  2017cites this paper
• Recurrent neural network weight estimation through backward tuning

  2017cites this paper
• Reproducibility and Comparability of Computational Models for Astrocyte Calcium Excitability

  2017cites this paper
• From bytes to insights with modelling as a service a new paradigm for computational modelling illustrated with PyRhO

  2016cites this paper
• Cross-species 3D virtual reality toolbox for visual and cognitive experiments.

  2016cites this paper
• PyRhO: A Multiscale Optogenetics Simulation Platform

  2016cites this paper
• How Modeling Standards, Software, and Initiatives Support Reproducibility in Systems Biology and Systems Medicine

  2016cites this paper
• The globus pallidus pars interna in goal‐oriented and routine behaviors: Resolving a long‐standing paradox

  2016cites this paper
• We Need New Scientific Languages to Harness the Complexity of Cognitive Development

  2016cites this paper
• pypet: A Python Toolkit for Data Management of Parameter Explorations

  2016cites this paper
• Neuroscience of decision making: from goal-directed actions to habits

  2016cites this paper
• What is computational reproducibility

  2016cites this paper
• Combiner les apprentissages motivés et associatifs

  2016cites this paper
• PyRhO: a virtual optogenetics laboratory

  2015cites this paper
• Why sharing matters for electrophysiological data analysis.

  2015cites this paper