NengoDL: Combining deep learning and neuromorphic modelling methods

May 28, 2018 · Entered Twilight · 🏛 Neuroinformatics

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Repo contents: .coveragerc, .gitignore, .travis.yml, CHANGELIST.md, CONTRIBUTORS.md, DEVELOP.md, LICENSE, README.rst, examples, nengo_spinnaker-data, nengo_spinnaker.conf.example, nengo_spinnaker, pytest.ini, regression-tests, requirements-test.txt, requirements.txt, setup.py, spinnaker_components, tests, tox.ini, utils

Authors Daniel Rasmussen arXiv ID 1805.11144 Category cs.NE: Neural & Evolutionary Cross-listed cs.AI Citations 99 Venue Neuroinformatics Repository https://github.com/project-rig/nengo_spinnaker ⭐ 14 Last Checked 15 days ago

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 .