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NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods

  • Daniel RasmussenEmail author
Software Original Article
  • 23 Downloads

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.

Keywords

Nengo TensorFlow Deep learning Computational neuroscience 

Notes

Acknowledgments

This work was supported by Applied Brain Research, Inc. and ONR MURI N00014-16-1-2832.

Compliance with Ethical Standards

Conflict of interests

DR is an employee/shareholder of Applied Brain Research, Inc., which owns the Nengo software package (including NengoDL). Nengo is free for research/personal/non-commercial use, but ABR charges a license fee for commercial use.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Applied Brain Research Inc.WaterlooCanada

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