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Nengo: a Python tool for building large-scale functional brain models
Open accessAbstract
Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world's largest functional brain model (Eliasmith et al., 2012). Simulating Spaun highlighted limitations in Nengo 1.4's ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.
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Cites
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The Soar Cognitive Architecture
Laird, J. E. (2012) · doi:10.7551/mitpress/7688.001.0001 · Cited by 1,108
Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Opportunities for neuromorphic computing algorithms and applications
Schuman, C. D., Kulkarni, S. R., Parsa, M., Mitchell, J. P., Date, P., & Kay, B. (2022) · Nature Computational Science · doi:10.1038/s43588-021-00184-y · Cited by 1,117
Neuromorphic photonic networks using silicon photonic weight banks
Tait, A. N., de Lima, T. F., Zhou, E., Wu, A. X., Nahmias, M. A., Shastri, B. J., & Prucnal, P. R. (2017) · Scientific Reports · doi:10.1038/s41598-017-07754-z · Cited by 849
Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook
Davies, M., Wild, A., Orchard, G., Sandamirskaya, Y., Guerra, G. A. F., Joshi, P., Plank, P., & Risbud, S. R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3067593 · Cited by 688
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Fang, W., Chen, Y., Ding, J., Yu, Z., Masquelier, T., Chen, D., Huang, L., Zhou, H., Li, G., & Tian, Y. (2023) · Science Advances · doi:10.1126/sciadv.adi1480 · Cited by 444
Metadata
- Authors
- Bekolay, T., Bergstra, J., Hunsberger, E., DeWolf, T., Stewart, T. C., Rasmussen, D., Choo, X., Voelker, A. R., & Eliasmith, C.
- Venue
- Frontiers in Neuroinformatics
- Publisher
- Frontiers Media SA
- Published
- 2014
- Volume
- 7
- Pages
- 48-48
- ISSN
- 1662-5196 (ISSN-L)
- OpenAlex
- W2065125569
- Cited by
- 500
- References
- 2
- FWCI
- 10.73
- Type
- journal article
- Language
- en
- Open access
- gold
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- Bekolay, T., Bergstra, J., Hunsberger, E., DeWolf, T., Stewart, T. C., Rasmussen, D., Choo, X., Voelker, A. R., & Eliasmith, C. (2014). Nengo: a Python tool for building large-scale functional brain models. Frontiers in Neuroinformatics. https://doi.org/10.3389/fninf.2013.00048
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