# Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification

arXiv: https://arxiv.org/abs/2102.10592 · HTML: https://mlsanity.com/paper/2102.10592/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 21 Feb 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network](https://mlsanity.com/paper/2003.02944/) (2020)

## Built on by (most cited first, 2 of 2)

- [Spike time displacement based error backpropagation in convolutional spiking neural networks](https://mlsanity.com/paper/2108.13621/) (2021)
- [Towards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI Hardware](https://mlsanity.com/paper/2407.08704/) (2024)

## Abstract (arXiv)

With the continued innovations of deep neural networks, spiking neural networks (SNNs) that more closely resemble biological brain synapses have attracted attention owing to their low power consumption.However, for continuous data values, they must employ a coding process to convert the values to spike trains.Thus, they have not yet exceeded the performance of artificial neural networks (ANNs), which handle such values directly.To this end, we combine an ANN and an SNN to build versatile hybrid neural networks (HNNs) that improve the concerned performance.To qualify this performance, MNIST and CIFAR-10 image datasets are used for various classification tasks in which the training and coding methods changes.In addition, we present simultaneous and separate methods to train the artificial and spiking layers, considering the coding methods of each.We find that increasing the number of artificial layers at the expense of spiking layers improves the HNN performance.For straightforward datasets such as MNIST, it is easy to achieve the same performance as ANNs by using duplicate coding and separate learning.However, for more complex tasks, the use of Gaussian coding and simultaneous learning is found to improve the accuracy of HNNs while utilizing a smaller number of artificial layers.