NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods


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

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    Note that this code is only intended to introduce the syntax; it would not result in particularly effective training if we were to run it. Better performance would require a more complicated Nengo model, which we are trying to avoid in this description. Various full functional examples can be found at

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    Note that although we are using Variables for all the Signals, not all Signals are trainable; we still only optimize the Signals corresponding to trainable parameters of the model (e.g., connection weights and biases).


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

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Correspondence to Daniel Rasmussen.

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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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Rasmussen, D. NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods. Neuroinform 17, 611–628 (2019).

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  • Nengo
  • TensorFlow
  • Deep learning
  • Computational neuroscience