神经网络吸引子元动力学方法论。研究慢适应过程如何塑造吸引子景观演化。适用于神经动力学、连续学习。触发词:吸引子、元动力学、神经动力学、attractor、metadynamics。
Scanned 9/11/2026
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---
name: attractor-metadynamics-neural
description: 神经网络吸引子元动力学方法论。研究慢适应过程如何塑造吸引子景观演化。适用于神经动力学、连续学习。触发词:吸引子、元动力学、神经动力学、attractor、metadynamics。
user-invocable: true
---
# Attractor Metadynamics - 吸引子元动力学
## 核心思想
慢适应过程(突触/内在可塑性)持续塑造神经网络吸引子景观。
**来源:** arXiv:1404.5417
**效用:** 0.90
---
## 实现
```python
import numpy as np
class AttractorMetadynamics:
def __init__(self, n=100):
self.W = np.random.randn(n, n) * 0.1
self.eta = 0.001
def dynamics(self, x, dt=0.1):
return x + (-x + np.tanh(self.W @ x)) * dt
def plasticity(self, x, dt=0.1):
self.W += self.eta * np.outer(x, x) * dt
```
---
## Activation Keywords
- 吸引子
- 元动力学
- 神经动力学
## Tools Used
- numpy
## Instructions for Agents
1. 理解慢快时间尺度
2. 跟踪吸引子演化
## Examples
研究持续学习中的记忆稳定性。
## 参考文献
- arXiv:1404.5417Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
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