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MemNNetSim: Memristive Nanowire Network Simulator. A proof-of-concept Python package for modelling and analyzing memristive random nanowire networks (NWNs). This package, developed by Marcus Kasdorf, was initiated from a summer research project in 2021 under the supervision of Dr. Claudia Gomes da Rocha at the University of Calgary.

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• Installation
• Usage
• Examples
• NWN Attributes
• Reference

Bibliography

Some main relevant papers that this work builds off of are:

• A. V. Avizienis, H. O. Sillin, C. Martin-Olmos, H. H. Shieh, M. Aono, A. Z. Stieg and J. K. Gimzewski, PLoS ONE, 2012, 7, e42772.
• D. B. Strukov, G. S. Snider, D. R. Stewart and R. S. Williams, Nature, 2008, 453, 80-83
• H. O. Sillin, R. Aguilera, H. -H. Shieh, A. V. Avizienis, M. Aono, A. Z. Stieg and J. K. Gimzewski, Nanotechnology, 2013, 24, 384004.
• L. Chen, C. Li, T. Huang, H. G. Ahmad and Y. Chen, Physics Letters A, 2014, 378, 2924-2930
• K. Fu, R. Zhu, A. Loeffler, J. Hochstetter, A. Diaz-Alvarez, A. Stieg, J. Gimzewski, T. Nakayama and Z. Kuncic, 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–8.

Acknowledgements

This package has emanated from research supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) - Discovery Grant, the Quantum City initiative, and the Alberta Innovates - Advance Program. We also acknowledge the Advanced Research Computing (ARC) facilities at the UofC, the Digital Research Alliance of Canada (former Compute Canada), and the CMC Microsystems for computational resources. We thank C. Soriano for the design and creation of the MemNNetSim logo and banner.