用神经元自激特性实现胜者为王的事件生成机制
A Winner-Takes-All Mechanism for Event Generation
- 通过全连接抑制+可设计兴奋互动，统一决策与节律生成
- 环振子模型实现相位与频率自适应调制，稳定可靠
- 适合神经形态系统与机器人控制，易实现且抗干扰强
我们提出一种新型中央模式发生器设计框架，利用神经元内在的反弹兴奋性与胜者为王计算相结合。该方法通过全连接抑制结构并辅以可设计的兴奋性交互，在简单而强大的网络架构中统一了决策与节律模式生成。该设计在实现简便性、可适应性和鲁棒性方面具有显著优势。我们通过环振子模型验证了其有效性，该模型表现出自适应相位与频率调制能力，表明该框架在神经形态系统与机器人应用中极具前景。
原文摘要 · Abstract (English)
We present a novel framework for central pattern generator design that leverages the intrinsic rebound excitability of neurons in combination with winner-takes-all computation. Our approach unifies decision-making and rhythmic pattern generation within a simple yet powerful network architecture that employs all-to-all inhibitory connections enhanced by designable excitatory interactions. This design offers significant advantages regarding ease of implementation, adaptability, and robustness. We demonstrate its efficacy through a ring oscillator model, which exhibits adaptive phase and frequency modulation, making the framework particularly promising for applications in neuromorphic systems and robotics.
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