NengoDL: Combining deep learning and neuromorphic modelling methods
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Abstract (arXiv)
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 .
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 May 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 6; 2024: 1; 2025: 1.
11 papers.
- 2021A Spiking Neural Network for Image Segmentationcited by 27
- 2021The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardwarecited by 19
- 2023A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practicescited by 8
- 2023Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentationcited by 5
- 2022Spiking Approximations of the MaxPooling Operation in Deep SNNscited by 3
- 2023Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detectioncited by 2
- 2023A differentiable brain simulator bridging brain simulation and brain-inspired computingcited by 2
- 2025Towards Neurocognitive-Inspired Intelligence: From AI's Structural Mimicry to Human-Like Functional Cognitioncited by 2
- 2023Tricking AI chips into Simulating the Human Brain: A Detailed Performance Analysiscited by 1
- 2023Joint ANN-SNN Co-training for Object Localization and Image Segmentationcited by 1
- 2024Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classificationcited by 0
- 2025Towards Neurocognitive-Inspired Intelligence: From AI's Structural Mimicry to Human-Like Functional Cognition9 Oct 2025
- 2024Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification3 Jun 2024
- 2023A differentiable brain simulator bridging brain simulation and brain-inspired computing9 Nov 2023
- 2023Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detection13 Jul 2023
- 2023A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices19 Mar 2023
- 2023Joint ANN-SNN Co-training for Object Localization and Image Segmentation10 Mar 2023
- 2023Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation14 Feb 2023
- 2023Tricking AI chips into Simulating the Human Brain: A Detailed Performance Analysis31 Jan 2023
- 2022Spiking Approximations of the MaxPooling Operation in Deep SNNs14 May 2022
- 2021A Spiking Neural Network for Image Segmentation16 Jun 2021
- 2021The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware13 Jun 2021