Skip to main content
Log in

HPC parallel implementation combining NEST Simulator and Python modules

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The paper presents a supercomputer parallel implementation of a brain inspired model combining a Python module simulating a layer of retina ganglion cells and NEST Simulator for a layer of spike timing neurons of Lateral geniculate nucleus (LGN) in the brain. Since the Python module appeared to be the bottleneck in the developed hierarchical model of human visual system, the proposed two parallel implementations of that module were combined with NEST Simulator in a single module. Simulations of developed module on different number of nodes and varying number of parallel processes were investigated and compared with respect to their time consumption.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Not applicable.

Code availability

We will provide the code of our software on github after acceptance of the manuscript for publication.

Notes

  1. https://www.nest-simulator.org/.

  2. http://www.opensourcebrain.org/projects/111.

  3. https://mpi4py.readthedocs.io/en/stable/index.html.

  4. http://www.hpc.acad.bg/system-1/.

References

  1. Plesser, H.E., Eppler, J.M., Morrison, A., Diesmann, M., Gewaltig, M.O.: Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers. Lect. Notes Comput. Sci. 4641, 672–681 (2007). https://doi.org/10.1007/978-3-540-74466-5_71

    Article  Google Scholar 

  2. Koprinkova-Hristova, P.D., Bocheva, N., Nedelcheva, S., Stefanova, M.: Spike timing neural model of motion perception and decision making. Front. Comput. Neurosci. 13, 20 (2019). https://doi.org/10.3389/fncom.2019.00020

    Article  Google Scholar 

  3. Nedelcheva, S., Koprinkova-Hristova, P.: Orientation selectivity tuning of a spike timing neural network model of the first layer of the human visual cortex. Stud. Comput. Intell. 793, 291–303 (2019)

    Article  Google Scholar 

  4. Dayan, P., Abbott, L.F.: Theoretical neuroscience: computational and mathematical modeling of neural systems, The MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  5. Grossberg, S., Pilly, P.K.: Temporal dynamics of decision-making during motion perception in the visual cortex. CAS/CNS technical report, Boston University Libraries OpenBU (2008)

  6. Kremkow, J., Perrinet, L.U., Monier, C., Alonso, J.-M., Aertsen, A., Frégnac, Y., Masson, G.S.: Push-pull receptive field organization and synaptic depression: Mechanisms for reliably encoding naturalistic stimuli in V1. Front. Neural Circ. 10, 37 (2016) https://doi.org/10.3389/fncir.2016.00037

    Article  Google Scholar 

  7. Troyer, T.W., Krukowski, A.E., Priebe, N.J., Miller, K.D.: Contrast invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity. J. Neurosci. 18, 5908–5927 (1998)

    Article  Google Scholar 

  8. Nedelcheva, S., Georgieva, K., Koprinkova-Hristova, P.: Parallel implementation of the model of retina ganglion cells layer. In: 2020 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2020, Art. No-9194616 (2020). https://doi.org/10.1109/INISTA49547.2020.9194616

  9. Martinez-Canada, P., Morillas, C., Pino, B., Ros, E., Pelayo, F.: A computational framework for realistic retina modeling. Int. J. Neural Syst. 26(07), 1650030 (2016)

    Article  Google Scholar 

  10. Dalcin, L., Kler, P., Paz, R., Cosimo, A. (2011) Parallel distributed computing using python, Adv. Water Resour. 34(9), 1124–1139. https://doi.org/10.1016/j.advwatres.2011.04.013

    Article  Google Scholar 

  11. Casti, A., Hayot, F., Xiao, Y., Kaplan, E.: A simple model of retina-LGN transmission. J. Comput. Neurosci. 24, 235–252 (2008)

    Article  Google Scholar 

  12. Jordan, J. et al.: NEST 2.18.0, Zenodo, (2019). https://doi.org/10.5281/zenodo.2605422

Download references

Acknowledgements

We acknowledge the provided access to the e-infrastructure of the NCHDC—part of the Bulgarian National Roadmap on RIs, with the financial support by the Grant No. DO\(1-387/18.12.2020\). It is a significantly extended version of our work presented at the International Conference INISTA'2020.

Funding

Simona Nedelcheva was supported by the Bulgarian National Scientific Program Young Scientists and Postdocs, Module Young Scientists.

Author information

Authors and Affiliations

Authors

Contributions

SN and PK-H developed the Python and NEST modules and conducted their parallel implementation and simulation experiments. MD and SI supported HPC implementation and software installation on Avitohol supercomputer.

Corresponding author

Correspondence to Petia Koprinkova-Hristova.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human and animal participants

The conducted research did not include humans or animal participants.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nedelcheva, S., Ivanovska, S., Durchova, M. et al. HPC parallel implementation combining NEST Simulator and Python modules. Cluster Comput 25, 1637–1644 (2022). https://doi.org/10.1007/s10586-021-03422-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03422-0

Keywords

Navigation