Adaptive Graph Diffusion Snn

---
name: adaptive-graph-diffusion-snn
description: "MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural  - Bio-inspired undirected diffusion for signal propagation in . Activation triggers: adaptive, graph, diffusion, neuroscience, SNN."
---

# MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural Networks

> Bio-inspired undirected diffusion for signal propagation in SNNs

## Metadata
- **Source**: arXiv:2603.14285
- **Authors**: Various researchers (from arXiv)
- **Published**: 2026-03-15

## Core Methodology

### Problem Statement
Spiking Neural Networks (SNNs) currently face a critical bottleneck: while individual neurons exhibit dynamic biological properties, their macro-scopic architectures remain confined within conventional graph structures. MorphSNN addresses this by incorporating bio-inspired undirected diffusion and structural plasticity into signal propagation. It introduces adaptive graph diffusion mechanisms and ...

### Key Innovations
- Bio-inspired undirected diffusion for signal propagation in SNNs
- Structural plasticity mechanism for dynamic network rewiring
- Adaptive graph diffusion beyond fixed graph structures
- Integration of synaptic and structural plasticity

## Implementation Guide

### Prerequisites
- PyTorch or other deep learning framework with SNN support
- Understanding of spiking neural networks and neuromorphic computing
- Familiarity with graph neural networks (for adaptive diffusion)

### Step-by-Step
1. **Understand the biological inspiration**: Study the brain mechanisms underlying the approach
2. **Implement core components**: Build the novel architectural elements described
3. **Integrate with existing SNN frameworks**: Adapt the approach to your SNN toolkit
4. **Evaluate on relevant benchmarks**: Test on tasks matching your target application

### Code Example
```python
# Pseudo-code structure - adapt to your framework
import torch
import torch.nn as nn

class Adaptive_Graph_Diffusion_Snn(nn.Module):
    def __init__(self, ...):
        super().__init__()
        # Initialize components based on paper
        
    def forward(self, x):
        # Forward pass implementing the methodology
        pass
```

## Applications
- Adaptive SNN architectures for dynamic environments
- Brain-inspired network design
- Neuromorphic computing with structural plasticity
- Flexible neural network topologies

## Pitfalls
- Structural plasticity increases computational complexity
- Requires careful tuning of diffusion parameters
- Network rewiring may affect stability during learning

## Related Skills
- adaptive-spiking-neuron-asn
- brain-inspired-snn-pattern-analysis
- spikingjelly-framework

## References
- arXiv:2603.14285: [MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural Networks](https://arxiv.org/abs/2603.14285)