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SNS-Toolbox: A Tool for Efficient Simulation of Synthetic Nervous Systems

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Biomimetic and Biohybrid Systems (Living Machines 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13548))

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Abstract

We introduce SNS-Toolbox, a Python software package for the design and simulation of networks of conductance-based neurons and synapses, also called Synthetic Nervous Systems (SNS). SNS-Toolbox implements non-spiking and spiking neurons in multiple software backends, and is capable of simulating networks with thousands of neurons in real-time. We benchmark the toolbox simulation speed across multiple network sizes, characterize upper limits on network size in various scenarios, and showcase the design of a two-layer convolutional network inspired by circuits within the Drosophila melanogaster optic lobe. SNS-Toolbox, as well as the code to generate all of the figures in this work, is located at https://github.com/wnourse05/SNS-Toolbox.

This work was funded by National Science Foundation (NSF) Award #1704436, as well as by NSF DBI 2015317 as part of the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program.

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Author information

Authors and Affiliations

  1. Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    William R. P. Nourse

  2. Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, 26506, USA

    Nicholas S. Szczecinski

  3. Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    Roger D. Quinn

Authors
  1. William R. P. Nourse
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  2. Nicholas S. Szczecinski
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  3. Roger D. Quinn
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Corresponding author

Correspondence to William R. P. Nourse .

Editor information

Editors and Affiliations

  1. Portland State University, Portland, OR, USA

    Alexander Hunt

  2. Technology Innovation Institute, Masdar City, United Arab Emirates

    Vasiliki Vouloutsi

  3. Case Western Reserve University, Cleveland, OH, USA

    Kenneth Moses

  4. Case Western Reserve University, Cleveland, OH, USA

    Roger Quinn

  5. Institute for Bioengineering of Cataloni, Barcelona, Spain

    Anna Mura

  6. University of Sheffield, Sheffield, UK

    Tony Prescott

  7. Radboud University, Nijmegen, The Netherlands

    Paul F. M. J. Verschure

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Nourse, W.R.P., Szczecinski, N.S., Quinn, R.D. (2022). SNS-Toolbox: A Tool for Efficient Simulation of Synthetic Nervous Systems. In: Hunt, A., et al. Biomimetic and Biohybrid Systems. Living Machines 2022. Lecture Notes in Computer Science(), vol 13548. Springer, Cham. https://doi.org/10.1007/978-3-031-20470-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-20470-8_4

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  • Online ISBN: 978-3-031-20470-8

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