一种基于改进Bhattacharyya距离的高斯网络协方差矩阵灵敏度分析方法

提出了一种改进的Bhattacharyya距离,用以度量2个协方差矩阵之间的差异性,简称为S_Σ距离。证明了该距离在正定矩阵空间中满足距离的3条性质：正定性、对称性以及三角不等性,并将S_Σ距离用于高斯网络协方差矩阵的灵敏度分析。数值实验结果表明,利用S_Σ距离得到的分析结果与KL距离、Bhattacharyya距离完全一致,由于S_Σ距离满足三角不等性,大大降低了矩阵的运算量。     

Voltage-controllable magnetic skyrmion dynamics for spiking neuron device applications

Voltage-controlled magnetic skyrmions have attracted special attention because they satisfy the requirements for well-controlled high-efficiency and energy saving for future skyrmion-based neuron device applications.In this work,we propose a compact leaky-integrate-fire(LIF)spiking neuron device by using the voltage-driven skyrmion dynamics in a multiferroic nanodisk structure.The skyrmion dynamics is controlled by well tailoring voltage-induced piezostrains,where the skyrmion radius can be effectively modulated by applying the piezostrain pulses.Like the biological neuron,the proposed skyrmionic neuron will accumulate a membrane potential as skyrmion radius is varied by inputting the continuous piezostrain spikes,and the skyrmion radius will return to the initial state in the absence of piezostrain.Therefore,this skyrmion radius-based membrane potential will reach a definite threshold value by the strain stimuli and then reset by removing the stimuli.Such the LIF neuronal functionality and the behaviors of the proposed skyrmionic neuron device are elucidated through the micromagnetic simulation studies.Our results may benefit the utilization of skyrmionic neuron for constructing the future energy-efficient and voltage-tunable spiking neural networks.