# Unsupervised Learning with Self-Organizing Spiking Neural Networks

arXiv: https://arxiv.org/abs/1807.09374 · HTML: https://mlsanity.com/paper/1807.09374/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2018.

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

- [Q-SpiNN: A Framework for Quantizing Spiking Neural Networks](https://mlsanity.com/paper/2107.01807/) (2021)
- [SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments](https://mlsanity.com/paper/2103.00424/) (2021)
- [lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents](https://mlsanity.com/paper/2205.12295/) (2022)
- [Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data](https://mlsanity.com/paper/2302.06279/) (2023)
- [Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization](https://mlsanity.com/paper/2302.05440/) (2023)
- [Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits](https://mlsanity.com/paper/2303.18187/) (2023)
- [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)
- [Parallel Hyperparameter Optimization Of Spiking Neural Network](https://mlsanity.com/paper/2403.00450/) (2024)
- [Learning Spiking Neural Network from Easy to Hard task](https://mlsanity.com/paper/2309.04737/) (2023)
- [Exploring the Potential of Spiking Neural Networks in UWB Channel Estimation](https://mlsanity.com/paper/2512.23975/) (2025)

## Abstract (arXiv)

We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are trained in an unsupervised manner to learn a self-organized lattice of filters via excitatory-inhibitory interactions among populations of neurons. We develop and test various inhibition strategies, such as growing with inter-neuron distance and two distinct levels of inhibition. The quality of the unsupervised learning algorithm is evaluated using examples with known labels. Several biologically-inspired classification tools are proposed and compared, including population-level confidence rating, and n-grams using spike motif algorithm. Using the optimal choice of parameters, our approach produces improvements over state-of-art spiking neural networks.