Plug-and-Play Homeostatic Spark: Zero-Cost Acceleration for SNN Training Across Paradigms

2025-12-04

AI Summary

Training spiking neural networks (SNNs) often suffers from slow convergence and poor training stability. To address this, we propose AHSAR—a zero-cost, plug-and-play adaptive homeostatic spike activity regulation mechanism. AHSAR requires no architectural modifications or gradient computation changes; instead, it estimates layer-wise homeostatic firing rates during the forward pass and applies bounded nonlinear threshold scaling. Additionally, it incorporates lightweight inter-layer diffusion and global gain adaptation across training epochs to dynamically calibrate neuronal activation levels. Crucially, AHSAR introduces no trainable parameters and is fully compatible with diverse SNN architectures, training paradigms (e.g., ANN-to-SNN conversion and direct training), and benchmark datasets. Experimental results demonstrate that AHSAR significantly accelerates convergence, improves training stability, enhances out-of-distribution robustness, and incurs negligible computational overhead.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Neuro-Symbolic Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Spiking neural networks offer event driven computation, sparse activation, and hardware efficiency, yet training often converges slowly and lacks stability. We present Adaptive Homeostatic Spiking Activity Regulation (AHSAR), an extremely simple plug in and training paradigm agnostic method that stabilizes optimization and accelerates convergence without changing the model architecture, loss, or gradients. AHSAR introduces no trainable parameters. It maintains a per layer homeostatic state during the forward pass, maps centered firing rate deviations to threshold scales through a bounded nonlinearity, uses lightweight cross layer diffusion to avoid sharp imbalance, and applies a slow across epoch global gain that combines validation progress with activity energy to tune the operating point. The computational cost is negligible. Across diverse training methods, SNN architectures of different depths, widths, and temporal steps, and both RGB and DVS datasets, AHSAR consistently improves strong baselines and enhances out of distribution robustness. These results indicate that keeping layer activity within a moderate band is a simple and effective principle for scalable and efficient SNN training.

Problem

• Stabilizes SNN training and accelerates convergence
• Maintains layer activity within moderate homeostatic band
• Enhances robustness across diverse architectures and datasets

Innovation

• AHSAR stabilizes SNN training without extra parameters
• It uses homeostatic layer activity regulation and threshold scaling
• Lightweight cross-layer diffusion prevents sharp activity imbalances