Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons

As the third generation of neural networks, spiking neural networks (SNNs) are dedicated to exploring more insightful neural mechanisms to achieve near-biological intelligence. Intuitively, biomimetic mechanisms are crucial to understanding and improving SNNs. For example, the associative long-term potentiation (ALTP) phenomenon suggests that in addition to learning mechanisms between neurons, there are associative effects within neurons. However, most existing methods only focus on the former and lack exploration of the internal association effects. In this paper, we propose a novel Adaptive Internal Association~(AIA) neuron model to establish previously ignored influences within neurons. Consistent with the ALTP phenomenon, the AIA neuron model is adaptive to input stimuli, and internal associative learning occurs only when both dendrites are stimulated at the same time. In addition, we employ weighted weights to measure internal associations and introduce intermediate caches to reduce the volatility of associations. Extensive experiments on prevailing neuromorphic datasets show that the proposed method can potentiate or depress the firing of spikes more specifically, resulting in better performance with fewer spikes. It is worth noting that without adding any parameters at inference, the AIA model achieves state-of-the-art performance on DVS-CIFAR10~(83.9\%) and N-CARS~(95.64\%) datasets.Comment: Accepted by ICASSP 202

Collaborative Target Tracking in Elliptic Coordinates: a Binocular Coordination Approach

This paper concentrates on the collaborative target tracking control of a pair of tracking vehicles with formation constraints. The proposed controller requires only distance measurements between tracking vehicles and the target. Its novelty lies in two aspects: 1) the elliptic coordinates are used to represent an arbitrary tracking formation without singularity, which can be deduced from inter-agent distances, and 2) the regulation of the tracking vehicle system obeys a binocular coordination principle, which simplifies the design of the control law by leveraging rich physical meanings of elliptic coordinates. The tracking system with the proposed controller is proven to be exponentially convergent when the target is stationary. When the target drifts with a small velocity, the desired tracking formation is achieved within a small margin proportional to the magnitude of the target's drift velocity. Simulation examples are provided to demonstrate the tracking performance of the proposed controller.Comment: 6 pages, 5 figure

Triangular lattice formation in robot swarms with minimal local sensing

Abstract The problem of triangular lattice formation in robot swarms has been investigated extensively in the literature, but the existing algorithms can hardly keep comparative performance from swarm simulation to real multi‐robot scenarios, due to the limited computation power or the restricted field of view (FOV) of robot sensors. Eventually, a distributed solution for triangular lattice formation in robot swarms with minimal sensing and computation is proposed and developed in this study. Each robot is equipped with a sensor with a limited FOV providing only a ternary digit of information about its neighbouring environment. At each time step, the motion command is directly determined by using only the ternary sensing result. The circular motions with a certain level of randomness lead the robot swarms to stable triangular lattice formation with high quality and robustness. Extensive numerical simulations and multi‐robot experiments are conducted. The results have demonstrated and validated the efficiency of the proposed approach. The minimised sensing and computation requirements pave the way for massive deployment at a low cost and implementation within swarms of miniature robots

Experimental and analytical study on factors influencing biomimetic undulating fin propulsion performance based on orthogonal experimental design

This paper presents an experimental study on the structural and motion parameters of a biomimetic mechanical fin, as actuators for biomimetic underwater vehicles, and their influences on its propulsion performance. Orthogonal experimental design method is employed to optimize the test scheme due to its capability of reducing experiment time and rapid determination of influencing factors. A L64 orthogonal array is adopted in our seven-factor mixed factorial experiment with three structure parameters and four motion parameters. The experimental results of range analysis and variance analysis are discussed. The statistic results show that the fin-ray configuration, sway frequency, wave number and rigidity of membrane are the primary factors affecting the integrated propelling performance of the biomimetic mechanical fin. Optimizing these parameters needs to compromise the propelling speed, efficiency and maximum output power of the actuator. The analytic results are also in agreement with theoretical analysis and simulation study on inclined-angle of fish fin-ray. Consequently, proper modification of the inclined angle of the fin-ray might improve not only the propelling speed and acceleration, but also the propelling efficiency