Morphsnn Adaptive Graph Diffusion

---
name: morphsnn-adaptive-graph-diffusion
description: "MorphSNN: Adaptive graph diffusion and structural plasticity for spiking neural networks. Graph-based SNN with dynamic structure. Activation: morphsnn, graph diffusion, structural plasticity, adaptive snn, graph neural network spiking."
---

# MorphSNN: Adaptive Graph Diffusion and Structural Plasticity

> Morphologically-inspired spiking neural network combining adaptive graph diffusion with structural plasticity for dynamic network reconfiguration.

## Metadata
- **Source**: arXiv:2603.14285v1
- **Published**: 2026-03-15
- **Categories**: cs.NE

## Core Methodology

### Key Innovation
Traditional SNNs have fixed architectures. MorphSNN introduces adaptive graph diffusion mechanisms combined with structural plasticity, enabling networks to dynamically reconfigure based on input patterns and learning requirements.

### Technical Framework
1. **Graph-Based Architecture**: Represent SNN as dynamic graph structure
2. **Adaptive Diffusion**: Learn optimal information flow paths
3. **Structural Plasticity**: Add/remove connections based on activity
4. **Morphological Inspiration**: Draw from neuronal morphology principles

## Implementation Guide

### Step-by-Step
1. **Initialize graph structure** with base connectivity
2. **Implement diffusion operators** for spike propagation
3. **Apply structural plasticity rules** based on activity correlations
4. **Train with adaptive learning** to optimize structure

### Applications
- Dynamic network optimization
- Task-specific architecture search
- Continual learning with structural changes
- Neuromorphic computing

## Pitfalls
- Graph operations add computational cost
- Structural changes require careful regularization
- Initialization affects final structure

## Related Skills
- brain-scale-snn-simulation
- spiking-computational-neuroscience-survey
- neuromorphic-computing