# An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections

arXiv: https://arxiv.org/abs/2207.02727 · HTML: https://mlsanity.com/paper/2207.02727/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2022.

## Built on by (most cited first, 13 of 13)

- [TIM: An Efficient Temporal Interaction Module for Spiking Transformer](https://mlsanity.com/paper/2401.11687/) (2024)
- [Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling](https://mlsanity.com/paper/2312.07625/) (2023)
- [Pretraining with Random Noise for Fast and Robust Learning without Weight Transport](https://mlsanity.com/paper/2405.16731/) (2024)
- [Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network](https://mlsanity.com/paper/2407.01645/) (2024)
- [Cross Knowledge Distillation between Artificial and Spiking Neural Networks](https://mlsanity.com/paper/2507.09269/) (2025)
- [Exploring Spatial-Temporal Dynamics in Event-based Facial Micro-Expression Analysis](https://mlsanity.com/paper/2508.11988/) (2025)
- [Brain-inspired and Self-based Artificial Intelligence](https://mlsanity.com/paper/2402.18784/) (2024)
- [Spiking neural networks with Hebbian plasticity for unsupervised representation learning](https://mlsanity.com/paper/2305.03866/) (2023)
- [Biologically Inspired Spiking Diffusion Model with Adaptive Lateral Selection Mechanism](https://mlsanity.com/paper/2503.23767/) (2025)
- [Learning with Spike Synchrony in Spiking Neural Networks](https://mlsanity.com/paper/2505.14841/) (2025)
- [Phasor Agents: Oscillatory Graphs with Three-Factor Plasticity and Sleep-Staged Learning](https://mlsanity.com/paper/2601.04362/) (2026)
- [Effects of Introducing Synaptic Scaling on Spiking Neural Network Learning](https://mlsanity.com/paper/2601.11261/) (2026)
- [NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework](https://mlsanity.com/paper/2605.15058/) (2026)

## Abstract (arXiv)

The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn. The human brain can quickly learn various conceptual knowledge in a self-organized and unsupervised manner, accomplished through coordinating various learning rules and structures in the human brain. Spike-timing-dependent plasticity (STDP) is a general learning rule in the brain, but spiking neural networks (SNNs) trained with STDP alone is inefficient and perform poorly. In this paper, taking inspiration from short-term synaptic plasticity, we design an adaptive synaptic filter and introduce the adaptive spiking threshold as the neuron plasticity to enrich the representation ability of SNNs. We also introduce an adaptive lateral inhibitory connection to adjust the spikes balance dynamically to help the network learn richer features. To speed up and stabilize the training of unsupervised spiking neural networks, we design a samples temporal batch STDP (STB-STDP), which updates weights based on multiple samples and moments. By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks. Our model achieves the current state-of-the-art performance of unsupervised STDP-based SNNs in the MNIST and FashionMNIST datasets. Further, we tested on the more complex CIFAR10 dataset, and the results fully illustrate the superiority of our algorithm. Our model is also the first work to apply unsupervised STDP-based SNNs to CIFAR10. At the same time, in the small-sample learning scenario, it will far exceed the supervised ANN using the same structure.