ArticleNeuroinformatics2019
NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods.
Article in Neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Spiking Neural Networks in Imaging: A Review and Case Study.Sensors (Basel, Switzerland) · 2025Review
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
- Signal-to-event encoding parameter selection for multiple event classification with spiking neural networks.Frontiers in neuroscience · 2025Article
- Exploring spiking neural networks for deep reinforcement learning in robotic tasks.Scientific reports · 2024Article
- The backpropagation algorithm implemented on spiking neuromorphic hardware.Nature communications · 2024Article
- LoCS-Net: Localizing convolutional spiking neural network for fast visual place recognition.Frontiers in neurorobotics · 2024Article
- Direct training high-performance deep spiking neural networks: a review of theories and methods.Frontiers in neuroscience · 2024Review
- Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning.Scientific reports · 2023Article
- Spiking CMOS-NVM mixed-signal neuromorphic ConvNet with circuit- and training-optimized temporal subsampling.Frontiers in neuroscience · 2023Article
- BIDL: a brain-inspired deep learning framework for spatiotemporal processing.Frontiers in neuroscience · 2023Article
- Spiking neural networks fine-tuning for brain image segmentation.Frontiers in neuroscience · 2023Article
- Spiking Neural Networks and Their Applications: A Review.Brain sciences · 2022Review
- Could simplified stimuli change how the brain performs visual search tasks? A deep neural network study.Journal of vision · 2022Article
- Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics.Patterns (New York, N.Y.) · 2022Article
- Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.Frontiers in computational neuroscience · 2022Article
- Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.Frontiers in neuroscience · 2022Article
- Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics.Frontiers in neurorobotics · 2020Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
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30972529What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.