Skip to main content
Springer Nature Link
Log in

Non-replicability circumstances in a neural network model with Hodgkin-Huxley-type neurons

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

Abstract

Building upon previous experiments can be used to accomplish new goals. In computing, it is imperative to reuse computer code to continue development on specific projects. Reproducibility is a fundamental building block in science, and experimental reproducibility issues have recently been of great concern. It may be surprising that reproducibility is also of concern in computational science. In this study, we used a previously published code to investigate neural network activity and we were unable to replicate our original results. This led us to investigate the code in question, and we found that several different aspects, attributable to floating-point arithmetic, were the cause of these replicability issues. Furthermore, we uncovered other manifestations of this lack of replicability in other parts of the computation with this model. The simulated model is a standard system of ordinary differential equations, very much like those commonly used in computational neuroscience. Thus, we believe that other researchers in the field should be vigilant when using such models and avoid drawing conclusions from calculations if their qualitative results can be substantially modified through non-reproducible circumstances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

References

  • Ben-ari, Y. (2002). Excitatory actions of gaba during development: The nature of the nurture. Nature Reviews Neuroscience, 3(9), 728–739.

    Article  CAS  Google Scholar 

  • Ben-ari, Y., Gaiarsa, J.-L., Tyzio, R., & Khazipov, R. (2007). GABA: A pioneer transmitter that excites immature neurons and generates primitive oscillations. Physiological Reviews, 87(4), 1215–1284.

    Article  CAS  Google Scholar 

  • Blanco, W., Bertram, R., & Tabak, J. (2017). The effects of GABAergic polarity changes on episodic neural network activity in developing neural systems. Frontiers in Computational Neuroscience, 11, 88.

    Article  Google Scholar 

  • Datta, B. N. (2010) Numerical linear algebra and applications. Siam.

  • Drummond, C. (2009) Replicability is not reproducibility: nor is it good science.

  • Hansel, D., Mato, G., Meunier, C., & Neltner, L. (1998). On numerical simulations of integrate-and-fire neural networks. Neural Computation, 10(2), 467–483.

    Article  CAS  Google Scholar 

  • Higham, N. J. (2002) Accuracy and stability of numerical algorithms. Siam. 0898718023.

  • IEEE Standard for Floating-Point Arithmetic. (2019) IEEE Std 754-2019 (Revision of IEEE 754-2008), p. 1–84.

  • Mcdougal, R. A., Bulanova, A. S., & Lytton, W. W. (2016). Reproducibility in Computational Neuroscience Models and Simulations. IEEE transactions on bio-medical engineering, 63(10), 2021–2035, 03/08.

    Article  Google Scholar 

  • Morrison, A., Straube, S., Plesser, H. E., & Diesmann, M. (2007). Exact subthreshold integration with continuous spike times in discrete-time neural network simulations. Neural Computation, 19(1), 47–79.

    Article  Google Scholar 

  • Nordlie, E., Gewaltig, M.-O., & Plesser, H. E. (2009). Towards reproducible descriptions of neuronal network models. PLoS computational biology, 5(8), e1000456.

    Article  Google Scholar 

  • Schling, B. (2011) The Boost C++ Libraries. XML Press, 262 p 0982219199, 9780982219195.

  • Shelley, M. J., & Tao, L. (2001). Efficient and accurate time-stepping schemes for integrate-and-fire neuronal networks. Journal of Computational Neuroscience, 11(2), 111–119.

    Article  CAS  Google Scholar 

  • Tabak, J., Mascagni, M., & Bertram, R. (2010). Mechanism for the universal pattern of activity in developing neuronal networks. Journal of Neurophysiology, 103(4), 2208–2221.

    Article  Google Scholar 

  • Waltemath, D., Adams, R., Beard, D. A., Bergmann, F. T., et al. (2011). Minimum information about a simulation experiment (MIASE). PLoS computational biology, 7(4), e1001122.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research started in 2016 when WB was hosted by the Mathematics department and the Institute of Molecular Biophysics at Florida State University. WB was also supported by a scholarship (Process #202320/2015-4) from the Brazilian National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq). PHL was supported by an undergraduate scientific research scholarship from CNPq in 2017 and from the State University of Rio Grande do Norte (UERN) in 2018.

Author information

Authors and Affiliations

  1. Computer Science Department, State University of Rio Grande do Norte, Natal, RN, Brazil

    Wilfredo Blanco, Paulo H. Lopes & Anderson Abner de S. Souza

  2. Bioinformatics Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    Wilfredo Blanco

  3. Computer Science Department, Florida State University, Tallahassee, FL, USA

    Michael Mascagni

  4. Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, MD, USA

    Michael Mascagni

Authors
  1. Wilfredo Blanco
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Paulo H. Lopes
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Anderson Abner de S. Souza
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Michael Mascagni
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Wilfredo Blanco.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interests.

Disclaimer

Certain commercial equipment, instruments, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology (NIST), nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

Additional information

Action Editor: David Terman

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(PDF 201 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Blanco, W., Lopes, P.H., de S. Souza, A.A. et al. Non-replicability circumstances in a neural network model with Hodgkin-Huxley-type neurons. J Comput Neurosci 48, 357–363 (2020). https://doi.org/10.1007/s10827-020-00748-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-020-00748-3

Keywords