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arXiv:1805.11144cs.NE

NengoDL: Combining deep learning and neuromorphic modelling methods

Daniel Rasmussen

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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 .

arXiv comments
22 pages, 9 figures; v2 fixes a link in the metadata; v3 minor text updates and updating code snippets to 2.0 syntax
Journal reference
DOI
Subjects
cs.NENeural and Evolutionary Computing cs.AIArtificial Intelligence

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