| name | spiking-neural-network-analysis |
| version | v1.0.0 |
| last_updated | "2026-04-06T00:00:00.000Z" |
| description | Analyze Spiking Neural Network (SNN) papers, extract technical patterns from knowledge graph, and identify reusable research methodologies for neuromorphic computing. |
Spiking Neural Network Analysis
Analyze SNN research papers from knowledge graph and extract reusable technical patterns.
Activation Keywords
- spiking neural network
- SNN analysis
- 脉冲神经网络分析
- spiking neuron
- neuromorphic computing
- SNN papers
Tools Used
exec - Run kg_tool and sqlite3 commands
read - Read skill docs and paper content
write - Save analysis reports
Workflow Decision Tree
User Request → Identify Task Type
├── Search Papers → Search kg.db for SNN papers
├── Analyze Patterns → Extract technical patterns
├── Knowledge Graph → Run PageRank/Louvain/Similarity
└── Generate Report → Create analysis summary
Core Commands
Step 1: Search SNN Entities
sqlite3 /Users/hiyenwong/wiki/kg.db \
"SELECT id, name FROM kg_entities WHERE name LIKE '%spiking%' OR name LIKE '%neuromorphic%'"
Step 2: Get Paper Details
sqlite3 /Users/hiyenwong/wiki/kg.db \
"SELECT id, name, properties FROM kg_entities WHERE entity_type='paper' AND name LIKE '%spiking%'"
Step 3: Knowledge Graph Analysis
/Users/hiyenwong/.openclaw/workspace/scripts/kg_tool/target/release/kg_tool pagerank --limit 10
/Users/hiyenwong/.openclaw/workspace/scripts/kg_tool/target/release/kg_tool communities --limit 10
/Users/hiyenwong/.openclaw/workspace/scripts/kg_tool/target/release/kg_tool search --query "spiking neural" --limit 10
Step 4: Extract Technical Patterns
Common SNN patterns:
- energy-efficient - Energy optimization
- brain-inspired - Biological inspiration
- forward-forward - Biologically plausible learning
- backpropagation-free - No backpropagation
- temporal dynamics - Time-series processing
- low-latency - Fast inference
Usage Examples
Example 1: Basic Analysis
User: "分析 SNN 论文"
Execute:
1. Search "spiking neural" papers
2. Get Top 5 paper properties
3. Run pagerank
4. Extract patterns
5. Generate report
Example 2: Pattern Extraction
User: "从 SNN 论文中提取技术模式"
Execute:
1. List all SNN papers
2. Parse keywords from properties
3. Identify common patterns
4. Create pattern summary
Example 3: Cross-domain Analysis
User: "找出与 SNN 相关的量子计算论文"
Execute:
1. Search "spiking" AND "quantum"
2. Find intersection entities
3. Analyze hybrid approaches
Key Findings from Analysis
Top SNN Papers in KG
| Paper | Category | Pattern |
|---|
| Energy-Efficient SNN | Medical AI | energy-efficient |
| Brain-Inspired Computing | Neuromorphic | brain-inspired |
| PSPM (Pre-Synaptic Pool) | Supervised Learning | forward-forward |
D2E Transfer (Direct-to-Event SNN Transfer)
arXiv: 2605.07207 — Luu et al. (2026), IEEE Signal Processing Letters
A critical SNN deployment pattern: converting direct-coded SNNs (floating-point inputs) to event-based representations (TTFS) for neuromorphic hardware.
Core approach: Self-Knowledge Distillation (SKD) — use the pretrained direct-coded SNN as its own teacher to guide event-based finetuning via KL-divergence regularization.
Key theorem: Cross-domain accuracy gap bounded by KL divergence between teacher/student output distributions + TV distance between input distributions.
Performance: SKD recovers 45-51pp on CIFAR-10 (vs naive TSF's 30-49pp), consistently outperforms across 9 architectures.
Theoretical limits: ~60% of input entropy is lost in TTFS encoding — even optimal transfer cannot match direct-coded teacher. Expect 15-20pp ceiling gap.
Pitfalls:
- Deep architectures suffer exponential spike rate collapse across layers
- Temperature scaling is critical for KL distillation effectiveness
- DVS sensor encoding gap is even larger than TTFS
SNN-Quantum Intersection
- Hybrid spiking-quantum CNN
- Quantum memristor for brain computing
- Neural operator quantum states
Related Skills
brain-connectivity-analysis - Brain network analysis
quantum-neural-hybrid - Quantum-classical NN
quantized-snn-hardware-optimization - Hardware acceleration
Limitations
- Requires kg.db with SNN papers
- Vector embeddings needed for similarity search
- Manual pattern interpretation
Resources
- Knowledge Graph:
/Users/hiyenwong/wiki/kg.db
- KG Tool:
/Users/hiyenwong/.openclaw/workspace/scripts/kg_tool/target/release/kg_tool
- Daily Research:
memory/2026-04-06.md