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

## TL;DR

This paper introduces a new threshold adaptation method for multi-layered spiking neural networks that improves classification accuracy and explores network sparsity, advancing energy-efficient neural computation.

## Contribution

The paper presents a novel timestamp-based threshold adaptation system for multi-layered SNNs, achieving state-of-the-art classification accuracy with unsupervised learning.

## Key findings

- Achieved 98.60% accuracy on MNIST with unsupervised SNN and SVM.
- Achieved 99.46% accuracy on Faces/Motorbikes dataset.
- Analyzed the impact of inhibition policies and STDP rules on network sparsity.

## Abstract

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.