convolution-delay-learning-snn

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

# 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