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

# 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