Gunasekaran, NallappanAli, M. SyedArik, SabriGhaffar, H. I. Abdul...
9页
查看更多>>摘要：This study deals with the finite-time synchronization problem of a class of switched complex dynamical networks (CDNs) with distributed coupling delays via sampled-data control. First, the dynamical model is studied with coupling delays in more detail. The sampling system is then converted to a continuous time-delay system using an input delay technique. We obtain some unique and less conservative criteria on exponential stability using the Lyapunov-Krasovskii functional (LKF), which is generated with a Kronecker product, linear matrix inequalities (LMIs), and integral inequality. Furthermore, some sufficient criteria are derived by an average dwell-time method and determine the finite-time boundedness of CDNs with switching signal. The proposed sufficient conditions can be represented in the form of LMIs. Finally, numerical examples are given to show that the suggested strategy is feasible. (c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：This paper proposes a novel memristive synaptic Hopfield neural network (MHNN) with time delay by using a memristor synapse to simulate the electromagnetic induced current caused by the membrane potential difference between two adjacent neurons. First, some sufficient conditions of zero bifurcation and zero-Hopf bifurcation are obtained by choosing time delay and coupling strength of memristor as bifurcation parameters. Then, the third-order normal form of zero-Hopf bifurcation is obtained. By analyzing the obtained normal form, six dynamic regions are found on the plane with coupling strength of memristor and time delay as abscissa and ordinate. There are some interesting dynamics in these areas, i.e., the coupling strength of memristor can affect the number and dynamics of system equilibrium, time delay can contribute to both trivial equilibrium and non-trivial equilibrium losing stability and generating periodic solutions. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：This paper considers joint learning of multiple sparse Granger graphical models to discover underlying common and differential Granger causality (GC) structures across multiple time series. This can be applied to drawing group-level brain connectivity inferences from a homogeneous group of subjects or discovering network differences among groups of signals collected under heterogeneous conditions. By recognizing that the GC of a single multivariate time series can be characterized by common zeros of vector autoregressive (VAR) lag coefficients, a group sparse prior is included in joint regularized least-squares estimations of multiple VAR models. Group-norm regularizations based on group-and fused-lasso penalties encourage a decomposition of multiple networks into a common GC structure, with other remaining parts defined in individual-specific networks. Prior information about sparseness and sparsity patterns of desired GC networks are incorporated as relative weights, while a non-convex group norm in the penalty is proposed to enhance the accuracy of network estimation in low-sample settings. Extensive numerical results on simulations illustrated our method's improvements over existing sparse estimation approaches on GC network sparsity recovery. Our methods were also applied to available resting-state fMRI time series from the ADHD-200 data sets to learn the differences of causality mechanisms, called effective brain connectivity, between adolescents with ADHD and typically developing children. Our analysis revealed that parts of the causality differences between the two groups often resided in the orbitofrontal region and areas associated with the limbic system, which agreed with clinical findings and data-driven results in previous studies. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：As a common approach of deep domain adaptation in computer vision, current works have mainly focused on learning domain-invariant features from different domains, achieving limited success in transfer learning. In this paper, we present a novel "deep adversarial transition learning "(DATL) framework that bridges the domain gap by generating some intermediate, transitional spaces between the source and target domains through the employment of adjustable, cross-grafted generative network stacks and effective adversarial learning between transitions. Specifically, variational auto-encoders (VAEs) are constructed for the domains, and bidirectional transitions are formed by cross-grafting the VAEs' decoder stacks. Generative adversarial networks are then employed to map the target domain data to the label space of the source domain, which is achieved by aligning the transitions initiated by different domains. This results in a new, effective learning paradigm, where training and testing are carried out in the associated transitional spaces instead of the original domains. Experimental results demonstrate that our method outperforms the state-of-the-art on a number of unsupervised domain adaptation benchmarks.(C) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Bio-inspired recipes are being introduced to artificial neural networks for the efficient processing of spatio-temporal tasks. Among them, Leaky Integrate and Fire (LIF) model is the most remarkable one thanks to its temporal processing capability, lightweight model structure, and well investigated direct training methods. However, most learnable LIF networks generally take neurons as independent individuals that communicate via chemical synapses, leaving electrical synapses all behind. On the contrary, it has been well investigated in biological neural networks that the inter-neuron electrical synapse takes a great effect on the coordination and synchronization of generating action potentials. In this work, we are engaged in modeling such electrical synapses in artificial LIF neurons, where membrane potentials propagate to neighbor neurons via convolution operations, and the refined neural model ECLIF is proposed. We then build deep networks using ECLIF and trained them using a back-propagation-through-time algorithm. We found that the proposed network has great accuracy improvement over traditional LIF on five datasets and achieves high accuracy on them. In conclusion, it reveals that the introduction of the electrical synapse is an important factor for achieving high accuracy on realistic spatio-temporal tasks.
查看更多>>摘要：Sign-based Stochastic Gradient Descents (Sign-based SGDs) use the signs of the stochastic gradients for communication costs reduction. Nevertheless, current convergence results of sign-based SGDs applied to the finite sum optimization are established on the bounded assumption of the gradient, which fails to hold in various cases. This paper presents a convergence framework about sign-based SGDs with the elimination of the bounded gradient assumption. The ergodic convergence rates are provided only with the smooth assumption of the objective functions. The Sign Stochastic Gradient Descent (siGNSGD) and its two variants, including majority vote and zeroth-order version, are developed for different application settings. Our framework also removes the bounded gradient assumption used in the previous analysis of these three algorithms. (C) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Neural activity emerges and propagates swiftly between brain areas. Investigation of these transient large-scale flows requires sophisticated statistical models. We present a method for assessing the statistical confidence of event-related neural propagation. Furthermore, we propose a criterion for statistical model selection, based on both goodness of fit and width of confidence intervals. We show that event-related causality (ERC) with two-dimensional (2D) moving average, is an efficient estimator of task-related neural propagation and that it can be used to determine how different cognitive task demands affect the strength and directionality of neural propagation across human cortical networks. Using electrodes surgically implanted on the surface of the brain for clinical testing prior to epilepsy surgery, we recorded electrocorticographic (ECoG) signals as subjects performed three naming tasks: naming of ambiguous and unambiguous visual objects, and as a contrast, naming to auditory description. ERC revealed robust and statistically significant patterns of high gamma activity propagation, consistent with models of visually and auditorily cued word production. Interestingly, ambiguous visual stimuli elicited more robust propagation from visual to auditory cortices relative to unambiguous stimuli, whereas naming to auditory description elicited propagation in the opposite direction, consistent with recruitment of modalities other than those of the stimulus during object recognition and naming. The new method introduced here is uniquely suitable to both research and clinical applications and can be used to estimate the statistical significance of neural propagation for both cognitive neuroscientific studies and functional brain mapping prior to resective surgery for epilepsy and brain tumors.
NSTL
Elsevier