Applied Brain Research Inc., Waterloo, ON, Canada.
Neuroinformatics. 2019 Oct;17(4):611-628. doi: 10.1007/s12021-019-09424-z.
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.
NengoDL 是一个软件框架，旨在结合神经形态建模和深度学习的优势。NengoDL 允许用户构建生物细节的神经模型，将这些模型与深度学习元素（如卷积网络）混合，然后在一个易于使用的统一框架中高效地模拟这些模型。此外，NengoDL 允许用户应用深度学习训练方法来优化生物神经模型的参数。本文介绍了 NengoDL 的基本使用示例、基准测试以及关键实现元素的详细信息。更多详细信息可以在 https://www.nengo.ai/nengo-dl 上找到。
