电感耦合约瑟夫森结：一个用于丰富神经形态动力学的平台
Inductively coupled Josephson junctions: a platform for rich neuromorphic dynamics
AI总结：
本文研究电感耦合约瑟夫森结系统的非线性神经计算动力学，揭示其脉冲、同步、多稳态及爆发行为，并提出新的爆发动力学机制。
摘要：
约瑟夫森结（JJs）本质上是一种神经形态硬件器件，能够模拟兴奋性和脉冲动力学。当它们相互耦合或与其他超导元件结合时，可以模拟生物神经元中发现的额外行为。从技术角度看，基于JJ的神经形态系统特别具有吸引力，因为它们具有太赫兹速度的处理能力，并且以接近零的功耗运行。在这项工作中，我们研究了一个电感耦合JJs系统，并重点关注其神经计算特性的非线性动力学方面。特别是，我们报告了与不变环上的鞍结分岔和2型兴奋性相关的脉冲行为、同步、首次脉冲延迟效应以及多稳态。特别强调了该系统能够再现的爆发动力学，并提出了一种超越先前工作所采用方法的新潜在机制。
英文摘要：
Josephson junctions (JJs) are by nature neuromorphic hardware devices capable of mimicking excitability and spiking dynamics. When coupled together or combined with other superconducting elements, they can emulate additional behaviors found in biological neurons. From a technological point of view, JJ-based neuromorphic systems are particularly appealing since they present THz-speed processing and they operate with near-zero power dissipation. In this work we study a system of inductively coupled JJs and focus on the nonlinear dynamical aspects of its neurocomputational properties. In particular, we report on spiking behavior related to a saddle-node off invariant cycle bifurcation and excitability type 2, synchronization, first spike latency effects, and multistability. Special emphasis is placed on the bursting dynamics the system is capable of reproducing, and a new underlying mechanism is proposed beyond the approach followed in prior works.
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