实验NbO x神经元的尖峰和破裂的表征和建模

Marie Drouhin, Shuaifei Li, M. Grelier, S. Collin, F. Godel, R. Elliman, B. Dlubak, J. Trastoy, D. Querlioz, J. Grollier
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引用次数: 0

摘要

硬件脉冲神经网络有望实现高能效的人工智能。在这种情况下,固态和可扩展的记忆电阻器可以用来模拟生物神经元的特性。然而,这些设备显示有限的神经元行为,必须集成在更复杂的电路中才能实现生物神经元的丰富动态。在这里,我们研究了一个NbO x记忆电阻神经元,它能够模拟许多神经元动力学,包括强直尖峰,随机尖峰,泄漏-整合-点火特征,尖峰延迟,时间整合。该装置还表现出相爆裂,这种特性在固态纳米神经元中几乎没有被观察和研究过。我们表明,我们可以通过非线性动力学模拟来重现和理解这种特殊的反应。这些结果表明,单个NbO x设备足以模拟丰富的神经元动力学集合,为实现可扩展和节能的神经形态计算范式铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterization and modeling of spiking and bursting in experimental NbO x neuron
Hardware spiking neural networks hold the promise of realizing artificial intelligence with high energy efficiency. In this context, solid-state and scalable memristors can be used to mimic biological neuron characteristics. However, these devices show limited neuronal behaviors and have to be integrated in more complex circuits to implement the rich dynamics of biological neurons. Here we studied a NbO x memristor neuron that is capable of emulating numerous neuronal dynamics, including tonic spiking, stochastic spiking, leaky-integrate-and-fire features, spike latency, temporal integration. The device also exhibits phasic bursting, a property that has scarcely been observed and studied in solid-state nano-neurons. We show that we can reproduce and understand this particular response through simulations using non-linear dynamics. These results show that a single NbO x device is sufficient to emulate a collection of rich neuronal dynamics that paves a path forward for realizing scalable and energy-efficient neuromorphic computing paradigms.
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CiteScore
5.90
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