spiking-neural-network-analysis

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

# Search knowledge graph
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

# Get paper properties (keywords, topics, category)
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

# PageRank
/Users/hiyenwong/.openclaw/workspace/scripts/kg_tool/target/release/kg_tool pagerank --limit 10

# Community detection
/Users/hiyenwong/.openclaw/workspace/scripts/kg_tool/target/release/kg_tool communities --limit 10

# Search
/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
• Qutrit entropy estimation via CNN (arXiv: 2606.20504) — Classical CNN estimates von Neumann entropy in multi-qutrit systems using only 12.5% of full tomography measurements. See qutrit-entropy-neural-estimation skill.

Key Recent Findings (2026-06-22)

Hybrid ANN-SNN Pipeline with Local Plasticity (arXiv: 2606.20151)

Core innovation: Couples pretrained EfficientNet encoder with CoLaNET spiking classifier via rate-coding. Trains SNN using local biologically-inspired learning rules, bypassing end-to-end backpropagation.

Performance: 99.09% on 64-class ImageNet benchmark — on par with conventional deep networks.

Why it matters: Demonstrates that pretrained ANN features + local plasticity can replace full backpropagation, enabling biologically-plausible yet high-performance SNNs.

Pattern: Pretrained Encoder → Rate Coding → Local Learning SNN — modular, energy-efficient, no gradient transport.

Biological Plausibility Assessment Framework (arXiv: 2606.17853)

Core innovation: Automated black-box assessment of spiking neuron models using Izhikevich firing pattern classification. Encodes 20 canonical patterns as objective functions for optimization.

Implementation: Python/PyTorch/Norse compatible. Treats neuron models as black boxes — no analytical modeling required.

Why it matters: Provides empirical, systematic way to quantify "how biological" a neuron model is. Useful for comparing neuromorphic hardware designs.

Pattern: Model → Pattern Encoding → Black-Box Optimization → Plausibility Score

Cross-domain extensions

Quantum-SNN Fusion

SNNs can be augmented with quantum computing for energy-efficient high-performance tasks:

• QDS-SNN: Quantum deep supervision + TSA-LIF neurons (arXiv: 2606.07657)
• QACM: Quantum-Assisted Classifier Modules using superposition/entanglement
• See quantum-neuromorphic-computing skill for the full methodology

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
• Reference Files:
  • references/functional-ensembles-1fc-groups.md - 1FC (First-order Functional Connectivity) groups framework from arXiv 2606.00073. Documents computational units in deep SNNs through inter-layer functional connectivity analysis, including methodology, experimental findings, and applications for network debugging, architecture optimization, and transfer learning.