NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods
NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods
复制标题
DOI：
10.1007/s12021-019-09424-z
复制
发表时间：
2019-10-01
期刊：
影响因子：
3
通讯作者：
Rasmussen, Daniel
中科院分区：
文献类型：
--
作者：
Rasmussen, Daniel
NengoDL是一个软件框架，旨在将神经形态建模和深度学习的优势联合收割机结合起来。NengoDL允许用户构建生物详细的神经模型，将这些模型与深度学习元素（如卷积网络）混合，然后在易于使用的统一框架中有效地模拟这些模型。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。在本文中，我们将介绍NengoDL的基本使用示例、基准测试和关键实现元素的详细信息。更多详情请访问。
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 .