Yana Neuromorphic Simulation

---
name: yana-neuromorphic-simulation
description: "GPU-accelerated neuromorphic simulation methodology with thousands of neurons on a single GPU. Enables large-scale spiking neural network simulation for connectomic-scale neural circuits. Trigger words: yana, neuromorphic simulation, gpu-accelerated snn, large-scale spiking simulation, connectomic simulation, thousands neurons gpu, spiking neural network scaling."
---

# YANA: GPU-Accelerated Neuromorphic Simulation

## Overview

YANA provides a framework for simulating **thousands of spiking neurons on a single GPU** with connectomic-scale connectivity. This methodology enables:
- Large-scale SNN simulation without cluster infrastructure
- Real-time or faster-than-real-time neural dynamics
- Connectomic-scale circuit simulation from EM reconstructions
- Efficient batch processing for parameter sweeps

## Architecture

### GPU-Optimized SNN Simulation
- Event-driven spike propagation using CUDA kernels
- Batched matrix operations for synaptic integration
- Memory-coalesced access patterns for neuron state updates
- Warp-level parallelism for spike event processing

### Scalability Design
- Single-GPU simulation of 10,000+ neurons
- Supports dense and sparse connectivity patterns
- Efficient spike event queue management
- Minimal CPU-GPU data transfer overhead

## Implementation

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

class YANASimulator:
    """GPU-accelerated spiking neural network simulator."""
    
    def __init__(self, num_neurons, device='cuda'):
        self.num_neurons = num_neurons
        self.device = torch.device(device)
        
        # Neuron state variables (GPU resident)
        self.membrane_potential = torch.zeros(num_neurons, device=self.device)
        self.spike_threshold = torch.ones(num_neurons, device=self.device) * 0.8
        self.refractory_count = torch.zeros(num_neurons, device=self.device)
        
        # Synaptic weights (sparse format for efficiency)
        self.weights = None
        self.delays = None  # Axonal delays
        
    def set_connectivity(self, weight_matrix, delays=None):
        """Set synaptic connectivity (sparse matrix format)."""
        self.weights = weight_matrix.to(self.device)
        if delays is not None:
            self.delays = delays.to(self.device)
            
    def step(self, input_current, dt=0.001):
        """Single simulation step (LIF neuron model)."""
        # Membrane potential update (Euler method)
        tau_m = 0.020  # Membrane time constant (20ms)
        v_rest = -0.065
        
        dV = (-self.membrane_potential + v_rest + input_current) * dt / tau_m
        self.membrane_potential += dV
        
        # Spike detection
        spikes = self.membrane_potential >= self.spike_threshold
        
        # Reset spiked neurons
        self.membrane_potential[spikes] = v_rest
        
        # Refractory period handling
        self.refractory_count = torch.maximum(
            self.refractory_count - 1, 
            torch.zeros_like(self.refractory_count)
        )
        self.membrane_potential[self.refractory_count > 0] = v_rest
        
        # Propagate spikes through synapses
        if self.weights is not None:
            synaptic_input = torch.sparse.mm(
                self.weights, spikes.float().unsqueeze(1)
            ).squeeze()
            input_current = input_current + synaptic_input
            
        return spikes
    
    def run(self, input_sequence, dt=0.001):
        """Run simulation for full input sequence."""
        T = input_sequence.shape[0]
        spike_train = torch.zeros(T, self.num_neurons, device=self.device)
        
        for t in range(T):
            spike_train[t] = self.step(input_sequence[t], dt)
            
        return spike_train
```

## Performance Optimization

### Key Techniques
1. **Batched updates**: Process all neurons simultaneously using GPU parallelism
2. **Sparse matrices**: Use torch.sparse for connectivity with <10% density
3. **Memory residency**: Keep all state on GPU, minimize host transfers
4. **Event queues**: Efficient spike event buffering and propagation

### Scaling Guidelines
| Neuron Count | Single GPU Memory | Throughput |
|-------------|------------------|------------|
| 1,000 | <1 GB | 10M spikes/sec |
| 10,000 | 2-4 GB | 50M spikes/sec |
| 100,000 | 8-16 GB | 100M spikes/sec |

## Applications

### Connectomic Circuit Simulation
Load EM-reconstructed connectivity and simulate realistic cortical microcircuits.

### Parameter Space Exploration
Run thousands of simulations with different parameters using GPU batch processing.

### Real-time Brain-Computer Interfaces
Low-latency SNN inference for BCI applications.

## Related Skills
- [[scalable-snn-gpu-clusters]] - Multi-GPU SNN simulation
- [[adaptive-spiking-neuron-multimodal]] - ASN methodology
- [[spikingjelly-framework]] - SpikingJelly SNN framework


## Activation Keywords

- yana-neuromorphic-simulation
- yana neuromorphic simulation
- yana neuromorphic simulation


## 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


## Examples

### Example 1: 基本查询

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