Morphsnn Structural Plasticity

---
name: morphsnn-structural-plasticity
description: "MorphSNN methodology - adaptive graph diffusion and structural plasticity for Spiking Neural Networks. Solves the mismatch between neuron-level dynamics and network-level static connectivity."
tags: ["SNN", "structural plasticity", "graph diffusion", "adaptive topology", "morphological learning", "ood-detection"]
paper_arxiv: "2603.14285v1"
paper_title: "MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural Networks"
authors: ["Yongsheng Huang", "Peibo Duan", "Yujie Wu", "Kai Sun", "Zhipeng Liu", "Jiaxiang Liu", "Guangyu Li", "Changsheng Zhang", "Bin Zhang", "Mingkan Xu"]
published: "2026-03-15"
category: "neuroscience"
---

# MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for SNNs

## Description

MorphSNN addresses the critical bottleneck in Spiking Neural Networks (SNNs) through adaptive graph diffusion and structural plasticity. While individual neurons exhibit dynamic biological properties, macroscopic architectures remain confined to conventional static connectivity patterns. This mismatch between neuron-level dynamics and network-level fixed connectivity eliminates brain-like lateral interactions, limiting SNN expressiveness and adaptability.

## The Problem: Static Architecture Limitation

### Traditional SNN Issues
```
Neuron level:
  ✓ Dynamic membrane potential
  ✓ Time-varying thresholds
  ✓ Spike-timing dependence

Network level:
  ✗ Fixed connection topology
  ✗ Predefined hierarchical structure
  ✗ No lateral connections

Result: Biological plasticity vs. artificial rigidity mismatch
```

### Biological Brain Characteristics
```
Structural Plasticity:
  - Synapse formation and elimination
  - Axon and dendrite remodeling
  - Dynamic connection topology changes

Functional consequences:
  - Network reorganization for new tasks
  - Physical encoding of memories
  - Morphological changes in development and learning
```

## MorphSNN Solution

### 1. Adaptive Graph Diffusion
```
Core idea: Let network topology evolve dynamically with activity and demands

Diffusion process:
  Input activity → Graph diffusion → Weight update → Topology reorganization
  
Mathematical form:
  dA/dt = -L·A + α·Activity + β·Plasticity
  
Where:
  A: Adjacency matrix
  L: Graph Laplacian
  Activity: Neuron activity driven
  Plasticity: Structural plasticity term
```

### 2. Structural Plasticity Mechanisms
```
Synaptogenesis:
  Condition: High neuron correlation but no connection
  Action: Form new synapse
  
Synaptic Pruning:
  Condition: Weak connection and low usage
  Action: Eliminate inefficient connection
  
Synaptic Potentiation:
  Condition: Frequent co-activation
  Action: Strengthen existing connection
```

### 3. Morphological Learning
```
Unlike learning only weights:
  Traditional: Fixed topology, learn w_ij
  MorphSNN: Learn topology + weights

Network morphology as learnable parameters:
  - Connection existence/non-existence
  - Connection strength
  - Connection delay
```

## Architecture Details

### Network Structure
```python
class MorphSNNLayer:
    """
    MorphSNN Layer: Dynamic topology SNN
    """
    
    def __init__(self, n_neurons, init_density=0.3):
        self.n_neurons = n_neurons
        
        # Dynamic adjacency matrix
        self.adjacency = self._initialize_sparse(n_neurons, init_density)
        
        # Synaptic weights
        self.weights = nn.Parameter(torch.randn(n_neurons, n_neurons))
        
        # Structural plasticity parameters
        self.synaptogenesis_threshold = 0.7
        self.pruning_threshold = 0.1
        self.plasticity_rate = 0.01
    
    def forward(self, spikes, dt=1.0):
        # Standard SNN forward
        currents = torch.matmul(self.adjacency * self.weights, spikes)
        
        # Update membrane potential
        self.membrane = self.membrane + dt * (-self.membrane + currents)
        
        # Fire
        output_spikes = (self.membrane >= self.threshold).float()
        self.membrane = self.membrane * (1 - output_spikes)
        
        # Record activity history (for structural plasticity)
        self.activity_history.append(spikes)
        
        return output_spikes
    
    def structural_plasticity_update(self):
        """
        Structural plasticity update (executed periodically)
        """
        # Compute neuron correlations
        activity = torch.stack(self.activity_history)
        correlation = torch.corrcoef(activity.T)
        
        # Synaptogenesis: High correlation but no connection → Form connection
        new_synapses = (correlation > self.synaptogenesis_threshold) & (self.adjacency == 0)
        self.adjacency[new_synapses] = 1.0
        
        # Synaptic pruning: Weak connection and low correlation → Eliminate
        weak_synapses = (torch.abs(self.weights) < self.pruning_threshold) & \
                       (correlation < 0.2)
        self.adjacency[weak_synapses] = 0.0
        
        # Graph diffusion: Smooth connection distribution
        self.adjacency = self._graph_diffusion(self.adjacency, steps=2)
        
        self.activity_history = []
```

## Key Advantages

### 1. Adaptive Connection Topology
```
Task A:
  Learned topology: Connection pattern suitable for task A
  
Switch to Task B:
  Structural plasticity reorganizes connections
  New topology: Suitable for task B
```

### 2. Emergent Lateral Interactions
```
Traditional SNN: Only feedforward/feedback connections
MorphSNN: Lateral connections form based on statistical dependencies

Effects:
  - Feature binding
  - Pattern completion
  - Associative memory
```

### 3. Hardware Efficiency
```
Sparse dynamic topology:
  - Reduce unnecessary connections
  - Computational sparsity
  - Storage efficiency
```

## Applications

### 1. Continual Learning
```
Problem: Catastrophic forgetting
MorphSNN solution:
  - Allocate new connections for new tasks
  - Preserve critical connections for old tasks
  - Structural isolation reduces interference
```

### 2. Neuromorphic Computing
```
Applicable platforms:
  - Intel Loihi (supports structural plasticity)
  - IBM TrueNorth
  - Custom neuromorphic chips
```

### 3. Brain Simulation
```
Increased biological fidelity:
  - Brain-like structural dynamics
  - Developmental learning
  - Damage recovery
```

## Implementation Details

### Hyperparameters
```yaml
# Structural plasticity
synaptogenesis_threshold: 0.7    # Synaptogenesis threshold
pruning_threshold: 0.1           # Pruning threshold
plasticity_rate: 0.01            # Plasticity learning rate
plasticity_interval: 100         # Update interval (steps)

# Graph diffusion
diffusion_steps: 2               # Diffusion steps
diffusion_rate: 0.1              # Diffusion rate

# Network initialization
initial_density: 0.3             # Initial connection density
max_density: 0.5                 # Maximum connection density
```

### Biological Plausibility
```
Corresponds to biological mechanisms:

Synaptogenesis ↔ Axonal growth cone guidance
Synaptic pruning ↔ Synapse elimination/weakening
Graph diffusion ↔ Diffusive neurotrophic factors
Weight learning ↔ Hebbian/anti-Hebbian plasticity
```

## Comparison with Other Approaches

| Method | Connection Topology | Learning Mechanism | Adaptability |
|--------|---------------------|---------------------|--------------|
| Traditional SNN | Fixed | Weights only | Low |
| Neural Architecture Search | Predefined search space | Structure + weights | Medium |
| Neural ODE | Implicit dynamics | Continuous | Medium |
| **MorphSNN** | **Dynamic evolution** | **Structure + weights** | **High** |

## References

1. Huang, Y. et al. (2026). MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural Networks. arXiv:2603.14285.

## Related Skills

- adaptive-spiking-neuron-asn
- ember-hybrid-snn-llm-architecture

## Activation Keywords

- morphsnn
- structural plasticity
- graph diffusion snn
- adaptive snn topology
- morphological learning
- dynamic network topology