By 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
- Graph-Based Architecture: Represent SNN as dynamic graph structure
- Adaptive Diffusion: Learn optimal information flow paths
- Structural Plasticity: Add/remove connections based on activity
- Morphological Inspiration: Draw from neuronal morphology principles
Implementation Guide
Step-by-Step
- Initialize graph structure with base connectivity
- Implement diffusion operators for spike propagation
- Apply structural plasticity rules based on activity correlations
- 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