MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks.

Spiking Neural Networks (SNNs) are considered more biologically realistic and power-efficient as they imitate the fundamental mechanism of the human brain. Backpropagation (BP) based SNN learning algorithms that utilize deep learning frameworks have achieved good performance. However, those BP-based algorithms partially ignore bio-interpretability. In modeling spike activity for biological plausible BP-based SNNs, we examine three properties: multiplicity, adaptability, and plasticity (MAP). Regarding multiplicity, we propose a Multiple-Spike Pattern (MSP) with multiple-spike transmission to improve model robustness in discrete time iterations. To realize adaptability, we adopt Spike Frequency Adaption (SFA) under MSP to reduce spike activities for enhanced efficiency. For plasticity, we propose a trainable state-free synapse that models spike response current to increase the diversity of spiking neurons for temporal feature extraction. The proposed SNN model achieves competitive performances on the N-MNIST and SHD neuromorphic datasets. In addition, experimental results demonstrate that the proposed three aspects are significant to iterative robustness, spike efficiency, and the capacity to extract spikes' temporal features. In summary, this study presents a realistic approach for bio-inspired spike activity with MAP, presenting a novel neuromorphic perspective for incorporating biological properties into spiking neural networks.

A Practical Approach for Environmental Flow Calculation to Support Ecosystem Management in Wujiang River, China.

To promote ecosystem protection in the Wujiang River, this paper proposes a practical approach for calculating the environmental flow. The proposed approach combines the idea of the "guarantee rate" of the flow duration curve (FDC) method and the grading idea of the Tennant method. A daily flow series of the Wujiang River was compiled from 1956 to 2019 and used to compare the effect of the proposed approach versus the traditional approaches in four selected sections along the river. The results show that the environmental flow of the Wujiang River can be divided into five levels by the T-FDC method, with a level-by-level disparity, and all levels can capture the temporal and spatial variability of river flow. Additionally, the calculated basic environmental flow process ranges between the historical minimum and second minimum monthly average flow, and the threshold width of the optimal flow is more reasonable than the Tennant method. The T-FDC method can provide technical support for Wujiang River ecosystem management and sustainable development.

Maximum Spreading of Impacting Ferrofluid Droplets under the Effect of Nonuniform Magnetic Field.

This article investigates the maximum spreading of ferrofluid droplets impacting on a hydrophobic surface under nonuniform magnetic fields. A generalized model for scaling the maximum spreading is developed. It is observed that, if the magnetic field strength is zero, a ferrofluid droplet not only demonstrates similar spreading dynamics as the water droplet but also obeys the same scaling law for the maximum spreading factor. Therefore, this article emphasizes the effects of magnetic field strength. In this regard, a dimensionless parameter (Nm) is introduced as the ratio between inertial force and Kelvin force, with an assumption that the kinetic energy mainly transforms to thermal energy. This parameter allows us to rescale all experimental data on a single curve with the Padé approximant, which is applicable to a wide range of impact velocities and magnetic field strengths.