# STDP Learning of Image Patches with Convolutional Spiking Neural Networks

arXiv: https://arxiv.org/abs/1808.08173 · HTML: https://mlsanity.com/paper/1808.08173/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2018.

## Built on by (most cited first, 8 of 8)

- [Q-SpiNN: A Framework for Quantizing Spiking Neural Networks](https://mlsanity.com/paper/2107.01807/) (2021)
- [lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents](https://mlsanity.com/paper/2205.12295/) (2022)
- [EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems](https://mlsanity.com/paper/2304.04039/) (2023)
- [Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks](https://mlsanity.com/paper/2212.12620/) (2022)
- [BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks](https://mlsanity.com/paper/2111.13188/) (2021)
- [tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2206.08656/) (2022)
- [BioLCNet: Reward-modulated Locally Connected Spiking Neural Networks](https://mlsanity.com/paper/2109.05539/) (2021)
- [Parallel Hyperparameter Optimization Of Spiking Neural Network](https://mlsanity.com/paper/2403.00450/) (2024)

## Abstract (arXiv)

Spiking neural networks are motivated from principles of neural systems and may possess unexplored advantages in the context of machine learning. A class of textitconvolutional spiking neural networks is introduced, trained to detect image features with an unsupervised, competitive learning mechanism. Image features can be shared within subpopulations of neurons, or each may evolve independently to capture different features in different regions of input space. We analyze the time and memory requirements of learning with and operating such networks. The MNIST dataset is used as an experimental testbed, and comparisons are made between the performance and convergence speed of a baseline spiking neural network.