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Self-organized learning in oscillatory neural networks (ONNs) using memristive signed couplings. Inhibitory (negative) weights enable anti-phase attractors, expanding accessible attractor structures beyond purely synchronous couplings for autonomous neuromorphic learning.
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| name | memristive-signed-couplings-onn |
| description | Self-organized learning in oscillatory neural networks (ONNs) using memristive signed couplings. Inhibitory (negative) weights enable anti-phase attractors, expanding accessible attractor structures beyond purely synchronous couplings for autonomous neuromorphic learning. |
| version | 1.0.0 |
| date | 2026-07-02T00:00:00.000Z |
| arxiv_id | 2607.00286 |
| tags | ["oscillatory neural networks","memristive devices","signed weights","inhibitory coupling","phase-coded memory","neuromorphic computing"] |
| activation_keywords | ["oscillatory neural network","ONN","memristive signed coupling","anti-phase attractor","phase-coded memory","inhibitory weight"] |
提出一种使用忆阻器边缘（memristive edges）实现抑制性耦合的神经形态原语（neuromorphic primitive），作为自主学习的潜在设计。关键创新：实现负权重使 ONN 可访问反相吸引子（anti-phase attractors），扩展了振荡器网络可用的吸引子结构类别。
振荡器节点 (oscillator nodes)
↕ 忆阻器边缘 (memristive edges)
↕ 抑制性耦合 (inhibitory couplings)
→ 符号有效权重 (signed effective weights)
| 权重类型 | 可用吸引子 | 记忆容量 |
|---|---|---|
| 纯正 | 同步相位 | 受限 |
| 符号 | 同步 + 反相 | 扩展 |
| 全符号 | 任意相位关系 | 最大 |
1. 初始化振荡器网络
2. 呈现训练模式（相位模式）
3. 忆阻器权重自调整（能量最小化）
4. 测试：噪声输入 → 去噪恢复
5. 验证：反相吸引子自主持续
oscillatory neural network, memristive signed coupling, anti-phase attractor, phase-coded memory, inhibitory coupling, neuromorphic primitive, self-organized learning, energy-minimizing dynamics, oscillator network