Abstract Spiking neural networks (SNNs) exhibit lower power consumption and faster inference speeds compared with artificial neural networks (ANNs), garnering significant research interest. ANN–SNN conversion is a popular method for object detection, image recognition, etc. Designing an architecture for image detection and recognition that balances high accuracy and low power consumption via ANN–SNN conversion remains a critical research challenge. In this paper, we propose a module called downward feature pyramid network to enhance the correlation between feature maps of different scales among networks. In response to the incompatibility between the efficient modules of ANN and the sparsity of SNN, we construct an SNN face detection and recognition framework called SNN-FDR based on RetinaFace and AdaFace. We conduct extensive experiments on the datasets WIDER FACE, LFW, AgeDB, CALFW, etc. Experimental results demonstrate a 95% reduction in energy consumption during the detection phase compared with the baseline, with only a 4.7% accuracy degradation. And in the recognition stage, we reduced energy consumption by 44.8% on high-quality datasets, while the average accuracy decreased only by 1.5%.

This paper studies the bipartite synchronization of signed networks with time-varying delays based on T-S fuzzy system, where the edges between nodes can be positive or negative. Assume that the signed graph of the network is structurally balanced. Firstly, the signed network system is described by T-S fuzzy model and then the control controller is used to control the network nodes, which can facilitate nodes to reach a synchronous state. Then some important lemmas and sufficient conditions are put forward to achieve bipartite synchronization of the signed networks. Finally, a numerical example is presented to certify the rationality of the theoretical results

This article is primarily concerned with the quasi-projective synchronization phenomenon between the a leader node and response nodes of an observer-based delayed dynamical network (DDN) instead of complete synchronization because of projective functional factor and parameters mismatch. First, a novel observer-based drive-response dynamical network (DN) with time-varying discrete-distributed delays and parameters mismatch is constructed to study its synchronization phenomena by introducing projective functional factor. Then, in order to obtain the sufficient criteria of quasi-projective synchronization for this system, the special impulsive control strategies and the definition of matrix measure are introduced in this article. After that, by the different impulsive phenomena and the properties of the projective functional factor, appropriate Lyapunov functional, Cauchy matrix, and inequality techniques are used to discuss and derive quasi-projective synchronization conditions of this observer-based delayed DN. In addition, some conclusions of synchronization for special DNs models are given as corollaries. Finally, an example with one leader node and four different response nodes and its corresponding simulation figures are given to demonstrate the obtained results.

This article studies the observer-based adaptive synchronization of multiagent systems (MASs) with unknown parameters under attacks. First, to estimate the state of agents, the observer for MAS is introduced. When disturbance, nonlinear function, and system model uncertainty are not considered, the nominal controller is proposed to achieve synchronization and state estimation. Then, in order to eliminate the effect of unknown parameters in the disturbance, nonlinear function, and system model uncertainty, the adaptive controller with switching term is introduced. However, the attack will lead to the destruction of the network topology so as the destruction of the nominal controller. By constructing an appropriate Lyapunov function, we analyze the effect caused by attacks, and the security control law is given to make sure the synchronization of the MASs under attacks. Finally, a numerical simulation is given to verify the validness of the obtained theorem.

This article focuses on the observer-based quasi-synchronization problem of delayed dynamical networks with parameter mismatch under impulsive effect. First, since the state of each node is unknown in the real situation, the state estimation strategy is proposed to estimate the state of each node, so as to design an appropriate synchronization controller. Then, the corresponding controller is constructed to synchronize the slave nodes with their leader node. In this article, we take the impulsive effect into consideration, which means that an impulsive signal will be applied to the system every so often. Due to the existence of parameter mismatch and time-varying delay, by constructing an appropriate Lyapunouv function, we will eventually obtain a differential equation with constant and time-varying delay terms. Then, we analyze its trajectory by introducing the Cauchy matrix and prove its boundedness by contradiction. Finally, a numerical simulation is presented to illustrate the validness of obtained results.

In this brief, hybrid impulsive and adaptive feedback controllers are simultaneously exerted on a general delayed memristive neural network (MNN) model to formulate a novel impulsive controlled MNN (IMNN) model with variable delays. By means of Lyapunov-Razumikhin technique and other analytical ways, several new stability criteria of the proposed IMNN model are obtained. In addition, by choosing appropriate impulses and external inputs, the convergence speed of IMNN can be increased, which implies that its dynamic behaviors will be optimized. Finally, the effectiveness of the obtained results is illustrated by one numerical example.

In this brief, we establish a novel complex-valued memristive recurrent neural network (CVMRNN) to study its stability. As a generalization of real-valued memristive neural networks, CVMRNN can be separated into real and imaginary parts. By means of M -matrix and Lyapunov function, the existence, uniqueness, and exponential stability of the equilibrium point for CVMRNNs are investigated, and sufficient conditions are presented. Finally, the effectiveness of obtained results is illustrated by two numerical examples.