Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install hiyenwong-ai-collection-collection-skills-convolution-delay-learning-snngit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-convolution-delay-learning-snn/SKILL.md---
name: convolution-delay-learning-snn
description: >-
Combines convolutional recurrent connections with DelRec delay learning mechanism
in recurrent spiking neural networks. Achieves 99% parameter savings and 52x faster
inference vs standard recurrent SNN while maintaining accuracy on audio classification.
Convolutional recurrent structure with learned axonal delays provides streamlined
architecture for resource-constrained edge systems.
Activation: convolution delay learning, DelRec, recurrent SNN audio, axonal delay learning,
efficient SNN architecture, edge SNN.
version: 1.0.0
metadata:
hermes:
tags: [snn, convolution, delay-learning, DelRec, edge-computing, audio-classification]
source_paper: "Combining Convolution and Delay Learning in Recurrent Spiking Neural Networks (arXiv:2604.15997)"
citations: 0
---
# Convolution + Delay Learning in Recurrent SNNs
## Overview
Extends DelRec (recurrent delay learning in SNNs) by combining convolutional recurrent
connections with the delay learning mechanism, achieving dramatic efficiency gains.
## Key Results
| Metric | Improvement |
|--------|-------------|
| Parameter reduction | ~99% savings |
| Inference speedup | 52x faster |
| Accuracy | Maintained vs standard recurrent |
## Architecture
- **Convolutional recurrent connections**: Replace fully recurrent with conv recurrent
- **DelRec delay learning**: Axonal delays learned at runtime with other parameters
- **Combined approach**: Convolution + delay learning in unified framework
## Implementation Pattern
```python
# Convolutional recurrent SNN with delay learning
# W_conv: convolutional recurrent weights (smaller footprint)
# Delays: learned parameters updated during training
# Combined update: weights + delays via surrogate gradient
```
## Task Domain
- Audio classification (tested)
- Other temporal classification tasks
- Edge deployment scenarios
## Pitfalls
- Convolution kernel size affects temporal receptive field
- Delay learning rate must be balanced with weight learning
- Audio preprocessing affects overall pipeline performance