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- Publisher
- Springer Journals
- Copyright
- Copyright © 2019 by Springer Science+Business Media, LLC, part of Springer Nature
- Subject
- Biomedicine; Neurosciences; Bioinformatics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Neurology
- ISSN
- 1539-2791
- eISSN
- 1559-0089
- DOI
- 10.1007/s12021-019-09424-z
- Publisher site
- See Article on Publisher Site
Abstract
NengoDL is a software framework designed to combine the strengths of neuromorphic modelling and deep learning. NengoDL allows users to construct biologically detailed neural models, intermix those models with deep learning elements (such as convolutional networks), and then efficiently simulate those models in an easy-to-use, unified framework. In addition, NengoDL allows users to apply deep learning training methods to optimize the parameters of biological neural models. In this paper we present basic usage examples, benchmarking, and details on the key implementation elements of NengoDL. More details can be found at https://www.nengo.ai/nengo-dl .
Journal
Neuroinformatics – Springer Journals
Published: Apr 10, 2019