Circulate Firing Snn Direct Training

---
name: circulate-firing-snn-direct-training
description: Direct training algorithm for SNNs with circulate-firing neurons and learnable surrogate gradients. Three core innovations for membrane potential dynamics optimization.
skill_type: research_methodology
paper_id: arXiv:2605.27412
paper_title: Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients
paper_date: 2026-05-14
authors: Feifan Zhou, Xiang Wei, Yang Liu, Qiang Yu
activation_keywords:
  - circulate-firing-snn
  - learnable-surrogate-gradient
  - direct-snn-training
  - membrane-potential-dynamics
  - spiking-transformer
related_domains:
  - spiking neural networks
  - neuromorphic computing
  - machine learning
  - neural architecture
---

# Circulate-Firing SNN Direct Training

Direct training algorithm for Spiking Neural Networks (SNNs) with three core innovations that leverage intrinsic membrane dynamics for performance improvement.

## Three Core Innovations

### 1. Circulate-Firing Spiking Neuron Model
- Enhanced information capacity leveraging membrane potentials effectively
- Rich dynamics utilizing full membrane potential trajectory
- Better information encoding through circulate dynamics

### 2. Time-Step-Wise Learnable Surrogate Gradient
- Adaptive gradients not fixed across all time steps
- Accurate estimation enabling precise gradient propagation
- Learning optimization improving training convergence

### 3. Positive-Negative Balanced Loss Function
- Equilibrium between positive and negative membrane potentials
- Performance boost for SNN systems
- Stability preventing potential imbalance issues

## Key Findings

1. Competitive performance across multiple datasets
2. Architecture generalization with Transformer architectures
3. Consistent outperformance of existing direct training methods
4. New pathway for advancing high-performance spiking architectures

## Applications

- Training SNNs with direct methods
- Improving SNN performance on benchmarks
- Implementing SNN-Transformer architectures
- Neuromorphic computing optimization research

## Related Skills
- snn-learning-survey
- surrogate-gradient-snn-training
- direct-to-event-snn-transfer
- spiking-transformer-unification

## References
- Paper arXiv 2605.27412 (14 May 2026)
- Category cs.NE cs.AI cs.LG
- DOI 10.48550/arXiv.2605.27412