PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing

2026-07-21 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

The Parallel Multi-compartment Spiking Neuron (PMSN) model is introduced to enhance Spiking Neural Networks' (SNNs) multi-scale temporal processing capabilities and training efficiency. PMSN mimics biological neurons by incorporating multiple interacting substructures, whose count can be flexibly adjusted to represent diverse temporal information. To mitigate increased computational complexity, the model integrates two parallelization techniques that decouple temporal dependencies, enabling parallel training across time steps. Experimental evaluations demonstrate PMSN's superiority, achieving over 10x simulation acceleration and a 30% accuracy improvement on the Sequential CIFAR10 dataset compared to the Leaky Integrate-and-Fire neuron, with comparable computational cost. It also reached 70.98% accuracy on ImageNet-1K. The model's dynamics reveal multi-timescale properties through damped oscillations and heterogeneous decay rates across neuron populations.

Key takeaway

For Machine Learning Engineers developing Spiking Neural Networks for complex temporal processing, you should consider adopting the Parallel Multi-compartment Spiking Neuron (PMSN) model. Its bio-inspired multi-compartment design and parallelization strategies significantly improve accuracy on long-sequence tasks like Sequential CIFAR10 by 30% and offer over 10x training acceleration. This allows you to tackle challenging real-world temporal problems more efficiently and effectively, balancing computational cost with superior performance.

Key insights

PMSN enhances SNNs' multi-scale temporal processing and training speed by emulating biological multi-compartment neurons with parallelization.

Principles

• Biological multi-compartment neuron structures improve temporal processing.
• Decoupling temporal dependencies enables parallel SNN training.
• Neuronal reset mechanisms are crucial for stable SNN information flow.

Method

PMSN uses a generalized multi-compartment model with flexible compartment numbers. It applies ZOH discretization and eigenvalue decomposition for hidden compartments, and a novel reset strategy for the output compartment, enabling parallel training via convolution and prefix sum.

In practice

• Implement multi-compartment SNNs for complex temporal tasks.
• Utilize parallelization techniques for faster SNN training on GPUs.
• Incorporate neuronal reset mechanisms for stable SNN performance.

Topics

• Spiking Neural Networks
• Multi-compartment Models
• Temporal Parallelization
• Neuromorphic Computing
• Multi-scale Processing
• GPU Acceleration

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.