Snn Learning Neuromorphic

---
name: snn-learning-neuromorphic
description: "Spiking Neural Network learning methods for neuromorphic computing — covering surrogate gradient, STDP, three-factor learning, DECOLLE, and sharpness-aware training. Use when training SNNs on neuromorphic hardware, implementing event-based learning, optimizing sparsity in spiking networks, or deploying energy-efficient AI. Trigger words: spiking neural network, SNN training, surrogate gradient, STDP, three-factor learning, DECOLLE, neuromorphic learning, event-based learning, temporal credit assignment"
---

# Spiking Neural Network Learning Methods

## Core Learning Paradigms

### 1. Surrogate Gradient Learning

Backpropagation through spikes using differentiable approximations:

```python
import torch
import torch.nn as nn

class SurrogateSpike(torch.autograd.Function):
    """Surrogate gradient for spiking function."""
    
    @staticmethod
    def forward(ctx, x, thresh=1.0):
        ctx.save_for_backward(x)
        ctx.thresh = thresh
        return (x > thresh).float()
    
    @staticmethod
    def backward(ctx, grad_output):
        x, = ctx.saved_tensors
        # Multi-Gaussian surrogate gradient
        sigma = 0.5
        grad = torch.exp(-((x - ctx.thresh) ** 2) / (2 * sigma**2))
        return grad_output * grad

# Usage
spike_fn = SurrogateSpike.apply
membrane_potential = torch.randn(10, 100)
spikes = spike_fn(membrane_potential)
```

### 2. Spike-Timing-Dependent Plasticity (STDP)

```python
def stdp_update(weights, pre_spikes, post_spikes, dt=1.0):
    """STDP weight update rule.
    
    Args:
        weights: Synaptic weight matrix (pre x post)
        pre_spikes: Presynaptic spike times
        post_spikes: Postsynaptic spike times
        dt: Time resolution
    """
    # Calculate timing differences
    delta_t = post_spikes[:, None] - pre_spikes[None, :]
    
    # LTP (pre before post)
    ltp = np.where(delta_t > 0, 
                   np.exp(-delta_t / 20.0),  # tau_plus = 20ms
                   0)
    
    # LTD (post before pre)
    ltd = np.where(delta_t < 0, 
                   -np.exp(delta_t / 20.0),  # tau_minus = 20ms
                   0)
    
    # Weight update
    dw = ltp + ltd
    return weights + dw * 0.01  # learning rate
```

### 3. Three-Factor Learning

```python
def three_factor_learning(weights, pre_spikes, post_spikes, modulatory_signal):
    """Three-factor learning rule with modulatory signal.
    
    Combines pre/post activity with global modulatory signal
    (e.g., dopamine, reward prediction error).
    """
    # Hebbian term (pre-post correlation)
    hebbian = pre_spikes[:, None] * post_spikes[None, :]
    
    # Modulated update
    dw = modulatory_signal * hebbian
    
    return weights + dw * learning_rate
```

### 4. DECOLLE (Deep Continuous Local Learning)

```python
class DECOLLELayer(nn.Module):
    """DECOLLE layer with local learning rule."""
    
    def __init__(self, n_in, n_out, n_classes):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(n_out, n_in))
        self.readout = nn.Parameter(torch.randn(n_classes, n_out))
        
    def forward(self, x):
        # Local loss for immediate feedback
        local_pred = self.readout @ x
        local_loss = F.cross_entropy(local_pred, target)
        
        # Synaptic update using local gradient
        with torch.no_grad():
            grad = torch.autograd.grad(local_loss, self.weight, retain_graph=True)[0]
            self.weight -= lr * grad
        
        return x @ self.weight.T
```

## Sharpness-Aware Surrogate Training (SAST)

For on-sensor SNN training with hardware-aware optimization:

```python
def sast_optimizer(model, data, rho=0.05, lr=0.01):
    """Sharpness-Aware Surrogate Training.
    
    Minimizes both loss and loss sharpness for robust training.
    """
    # First forward-backward pass
    loss = compute_loss(model, data)
    loss.backward()
    
    # Compute perturbation for sharpness
    with torch.no_grad():
        perturbations = {}
        for name, param in model.named_parameters():
            if param.grad is not None:
                perturbations[name] = rho * param.grad / (param.grad.norm() + 1e-8)
                param.data += perturbations[name]
    
    # Second forward pass with perturbed weights
    loss_sharp = compute_loss(model, data)
    
    # Restore weights and update
    with torch.no_grad():
        for name, param in model.named_parameters():
            param.data -= perturbations.get(name, 0)
        
        # Combined update
        for name, param in model.named_parameters():
            if param.grad is not None:
                param.data -= lr * (param.grad + loss_sharp.grad)
```

## Key Design Patterns

### Temporal Credit Assignment

1. **Event-based backpropagation**: Only propagate through spike events
2. **Memory-efficient replay**: Store only spike times, not full membrane trajectories
3. **Local approximations**: Use eligibility traces for online learning

### Energy-Efficient Training

1. **Sparse connectivity**: Prune weak synapses during training
2. **Low-precision weights**: Quantize to 4-8 bit for neuromorphic deployment
3. **Event-driven computation**: Skip computations for silent neurons

## Deployment Pipeline

```python
# Training pipeline
def train_snn(model, train_loader, epochs=100):
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
    surrogate = SurrogateSpike.apply
    
    for epoch in range(epochs):
        for data, target in train_loader:
            optimizer.zero_grad()
            
            # Forward pass with surrogate gradients
            output = model(data, surrogate_fn=surrogate)
            loss = F.cross_entropy(output, target)
            
            loss.backward()
            optimizer.step()
            
        # Optional: Apply STDP for biological plausibility
        if epoch % 10 == 0:
            apply_stdp_regularization(model)

# Export to neuromorphic format
def export_to_neuromorphic(model, format='loihi'):
    """Export trained SNN to neuromorphic hardware format."""
    if format == 'loihi':
        return export_to_loihi(model)
    elif format == 'speck':
        return export_to_speck(model)
    elif format == 'dynap':
        return export_to_dynapcnns(model)
```

## Performance Optimization

1. **Sparsity analysis**: Monitor firing rates and synaptic sparsity
2. **Temporal compression**: Reduce time steps without accuracy loss
3. **Hardware-aware quantization**: Match precision to target hardware

## Activation Keywords

- spiking neural network, SNN training, surrogate gradient
- STDP, three-factor learning, DECOLLE, neuromorphic learning
- event-based learning, temporal credit assignment
- sharpness-aware training, on-sensor training


## Tools Used

- `read` - 读取技能文档
- `write` - 创建输出
- `exec` - 执行相关命令


## Instructions for Agents

1. 理解技能的核心方法论
2. 根据用户问题提供针对性回答
3. 遵循最佳实践


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