用磁记忆电感构建新型类脑计算，可实现生物级时间记忆与预测。
Beyond Memristor: Neuromorphic Computing Using Meminductor
- 用磁芯线圈实现电感随电荷变化的忆磁特性，形成新型忆磁元件。
- 实验再现阿米巴原虫的记时、记忆和预见行为，验证其类脑能力。
- 适用于需要时间动态的神经形态计算，适合脑启发系统研究者。
忆阻器（具有记忆的电阻）、忆感器（具有记忆的电感）和忆容器（具有记忆的电容）在新型计算架构中扮演不同角色。我们发现，带有磁芯的线圈本质上是忆感器，其电感L(q)依赖于电荷q，线圈中电流的历史通过磁芯的磁化状态被记忆。这种忆感器在类脑计算、深度学习及脑启发计算中具有独特作用，因为神经形态RLC电路的时间常数由电感和电容共同决定，而非电阻。作为实验验证，该新型忆感器成功再现了阿米巴原虫的记事、计时与预见行为。结论表明，超越忆阻器的计算范式在理论上合理且实验上可行。
原文摘要 · Abstract (English)
Memristor (resistor with memory), inductor with memory (meminductor) and capacitor with memory (memcapacitor) have different roles to play in novel computing architectures. We found that a coil with a magnetic core is an inductor with memory (meminductor) in terms of its inductance L(q) being a function of the charge q. The history of the current passing through the coil is remembered by the magnetization inside the magnetic core. Such a meminductor can play a unique role (that cannot be played by a memristor) in neuromorphic computing, deep learning and brain inspired since the time constant of a neuromorphic RLC circuit is jointly determined by the inductance and capacitance, rather than the resistance. As an experimental verification, this newly invented meminductor was used to reproduce the observed biological behaviour of amoebae (the memorizing, timing and anticipating mechanisms). In conclusion, a beyond memristor computing paradigm is theoretically sensible and experimentally practical.
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