18236

NengoDL: Combining deep learning and neuromorphic modelling methods

Daniel Rasmussen
Applied Brain Research Inc., Waterloo, ON, Canada
arXiv:1805.11144 [cs.NE], (30 May 2018)

@article{rasmussen2018nengodl,

   title={NengoDL: Combining deep learning and neuromorphic modelling methods},

   author={Rasmussen, Daniel},

   year={2018},

   month={may},

   archivePrefix={"arXiv"},

   primaryClass={cs.NE}

}

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.
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