# Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP

arXiv: https://arxiv.org/abs/1904.01908 · HTML: https://mlsanity.com/paper/1904.01908/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks](https://mlsanity.com/paper/2103.12593/) (2021)
- [Spiking Neural Networks for Frame-based and Event-based Single Object Localization](https://mlsanity.com/paper/2206.06506/) (2022)
- [Co-learning synaptic delays, weights and adaptation in spiking neural networks](https://mlsanity.com/paper/2311.16112/) (2023)
- [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16235/) (2023)
- [2D versus 3D Convolutional Spiking Neural Networks Trained with Unsupervised STDP for Human Action Recognition](https://mlsanity.com/paper/2205.13474/) (2022)
- [Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks](https://mlsanity.com/paper/2308.02194/) (2023)
- [A Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDP](https://mlsanity.com/paper/2105.14740/) (2021)
- [Memory via Temporal Delays in weightless Spiking Neural Network](https://mlsanity.com/paper/2202.07132/) (2022)
- [Neuronal Competition Groups with Supervised STDP for Spike-Based Classification](https://mlsanity.com/paper/2410.17066/) (2024)
- [Deep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learning](https://mlsanity.com/paper/2105.05609/) (2021)
- [A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning](https://mlsanity.com/paper/2111.00791/) (2021)
- [Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition](https://mlsanity.com/paper/2306.13783/) (2023)
- [S3TC: Spiking Separated Spatial and Temporal Convolutions with Unsupervised STDP-based Learning for Action Recognition](https://mlsanity.com/paper/2309.12761/) (2023)
- [Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity](https://mlsanity.com/paper/2211.05412/) (2022)

## Abstract (arXiv)

Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware. However, the performance of these models is currently behind traditional methods. Introducing multi-layered SNNs is a promising way to reduce this gap. We propose in this paper a new threshold adaptation system which uses a timestamp objective at which neurons should fire. We show that our method leads to state-of-the-art classification rates on the MNIST dataset (98.60%) and the Faces/Motorbikes dataset (99.46%) with an unsupervised SNN followed by a linear SVM. We also investigate the sparsity level of the network by testing different inhibition policies and STDP rules.