将尖峰神经元连接到尖峰忆阻器网络会改变忆阻器的动态

Deborah L. Gater, Attya Iqbal, Jeffrey Davey, E. Gale
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引用次数: 16

摘要

忆阻器被认为是神经形态的计算元件。神经元的峰值时间依赖性可塑性和霍奇金-赫胥黎模型都已被记忆电阻理论有效地建模。记忆电阻器的直流响应是电流尖峰。基于这三个事实,我们认为忆阻器可以很好地直接与神经元连接。在本文中,我们证明了将尖峰记忆电阻网络连接到尖峰神经元细胞会引起记忆电阻网络动力学的变化:引起与记忆电阻状态变化一致的电流衰减率的变化;呈现更线性的I-t动力学;增加记忆电阻的尖峰率,这是与尖峰神经元相互作用的结果。这表明神经元能够直接与忆阻器交流,而不需要计算机翻译。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Connecting spiking neurons to a spiking memristor network changes the memristor dynamics
Memristors have been suggested as neuromorphic computing elements. Spike-time dependent plasticity and the Hodgkin-Huxley model of the neuron have both been modelled effectively by memristor theory. The d.c. response of the memris-tor is a current spike. Based on these three facts we suggest that memristors are well-placed to interface directly with neurons. In this paper we show that connecting a spiking memristor network to spiking neuronal cells causes a change in the memristor network dynamics by: causing a change in current decay rate consistent with a change in memristor state; presenting more-linear I-t dynamics; and increasing the memristor spiking rate, as a consequence of interaction with the spiking neurons. This demonstrates that neurons are capable of communicating directly with memristors, without the need for computer translation.
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