# Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition

arXiv: https://arxiv.org/abs/1602.01510 · HTML: https://mlsanity.com/paper/1602.01510/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2016.

## Built on by (most cited first, 7 of 7)

- [Hoyer regularizer is all you need for ultra low-latency spiking neural networks](https://mlsanity.com/paper/2212.10170/) (2022)
- [HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2107.11979/) (2021)
- [BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks](https://mlsanity.com/paper/2111.13188/) (2021)
- [Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization](https://mlsanity.com/paper/2109.11045/) (2021)
- [BioLCNet: Reward-modulated Locally Connected Spiking Neural Networks](https://mlsanity.com/paper/2109.05539/) (2021)
- [N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2](https://mlsanity.com/paper/2408.13379/) (2024)
- [When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks](https://mlsanity.com/paper/2312.06900/) (2023)

## Abstract (arXiv)

We present a spike-based unsupervised regenerative learning scheme to train Spiking Deep Networks (SpikeCNN) for object recognition problems using biologically realistic leaky integrate-and-fire neurons. The training methodology is based on the Auto-Encoder learning model wherein the hierarchical network is trained layer wise using the encoder-decoder principle. Regenerative learning uses spike-timing information and inherent latencies to update the weights and learn representative levels for each convolutional layer in an unsupervised manner. The features learnt from the final layer in the hierarchy are then fed to an output layer. The output layer is trained with supervision by showing a fraction of the labeled training dataset and performs the overall classification of the input. Our proposed methodology yields 0.92%/29.84% classification error on MNIST/CIFAR10 datasets which is comparable with state-of-the-art results. The proposed methodology also introduces sparsity in the hierarchical feature representations on account of event-based coding resulting in computationally efficient learning.