# Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration

arXiv: https://arxiv.org/abs/2304.12214 · HTML: https://mlsanity.com/paper/2304.12214/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2023.

## Built on by (most cited first, 10 of 10)

- [AutoReP: Automatic ReLU Replacement for Fast Private Network Inference](https://mlsanity.com/paper/2308.10134/) (2023)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs](https://mlsanity.com/paper/2307.08197/) (2023)
- [Neuroplasticity in Artificial Intelligence -- An Overview and Inspirations on Drop In & Out Learning](https://mlsanity.com/paper/2503.21419/) (2025)
- [Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks](https://mlsanity.com/paper/2311.16141/) (2023)
- [GI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge](https://mlsanity.com/paper/2405.03516/) (2024)
- [Evolved Developmental Artificial Neural Networks for Multitasking with Advanced Activity Dependence](https://mlsanity.com/paper/2407.10359/) (2024)
- [Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World](https://mlsanity.com/paper/2403.20047/) (2024)
- [Towards Zero Memory Footprint Spiking Neural Network Training](https://mlsanity.com/paper/2308.08649/) (2023)
- [Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network](https://mlsanity.com/paper/2511.01553/) (2025)

## Abstract (arXiv)

Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event-driven operation and sparse activities. As artificial intelligence (AI) becomes ever more democratized, there is an increasing need to execute SNN models on edge devices. Existing works adopt weight pruning to reduce SNN model size and accelerate inference. However, these methods mainly focus on how to obtain a sparse model for efficient inference, rather than training efficiency. To overcome these drawbacks, in this paper, we propose a Neurogenesis Dynamics-inspired Spiking Neural Network training acceleration framework, NDSNN. Our framework is computational efficient and trains a model from scratch with dynamic sparsity without sacrificing model fidelity. Specifically, we design a new drop-and-grow strategy with decreasing number of non-zero weights, to maintain extreme high sparsity and high accuracy. We evaluate NDSNN using VGG-16 and ResNet-19 on CIFAR-10, CIFAR-100 and TinyImageNet. Experimental results show that NDSNN achieves up to 20.52% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99%) as compared to other SOTA methods (e.g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN). In addition, the training cost of NDSNN is only 40.89% of the LTH training cost on ResNet-19 and 31.35% of the LTH training cost on VGG-16 on CIFAR-10.