# NengoDL: Combining deep learning and neuromorphic modelling methods

arXiv: https://arxiv.org/abs/1805.11144 · HTML: https://mlsanity.com/paper/1805.11144/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 11 of 11)

- [A Spiking Neural Network for Image Segmentation](https://mlsanity.com/paper/2106.08921/) (2021)
- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices](https://mlsanity.com/paper/2303.10780/) (2023)
- [Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation](https://mlsanity.com/paper/2302.07328/) (2023)
- [Spiking Approximations of the MaxPooling Operation in Deep SNNs](https://mlsanity.com/paper/2205.07076/) (2022)
- [Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detection](https://mlsanity.com/paper/2307.08501/) (2023)
- [A differentiable brain simulator bridging brain simulation and brain-inspired computing](https://mlsanity.com/paper/2311.05106/) (2023)
- [Towards Neurocognitive-Inspired Intelligence: From AI's Structural Mimicry to Human-Like Functional Cognition](https://mlsanity.com/paper/2510.13826/) (2025)
- [Tricking AI chips into Simulating the Human Brain: A Detailed Performance Analysis](https://mlsanity.com/paper/2301.13637/) (2023)
- [Joint ANN-SNN Co-training for Object Localization and Image Segmentation](https://mlsanity.com/paper/2303.12738/) (2023)
- [Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification](https://mlsanity.com/paper/2407.08746/) (2024)

## 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 .