Neural Networks最新文献_第2页

TSOM: Small object motion detection neural network inspired by avian visual circuit TSOM：受鸟类视觉回路启发的小物体运动检测神经网络。

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-09 DOI: 10.1016/j.neunet.2024.106881

Pingge Hu , Xiaoteng Zhang , Mengmeng Li , Yingjie Zhu , Li Shi

{"title":"TSOM: Small object motion detection neural network inspired by avian visual circuit","authors":"Pingge Hu , Xiaoteng Zhang , Mengmeng Li , Yingjie Zhu , Li Shi","doi":"10.1016/j.neunet.2024.106881","DOIUrl":"10.1016/j.neunet.2024.106881","url":null,"abstract":"<div><div>Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and the Retina-OT-Rt visual circuit of birds is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system. In this paper, we conducted mathematical description based on extensive studies of the biological mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a novel tectum small object motion detection neural network (TSOM). The TSOM neural network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each corresponding to neurons in the visual pathway for precise topographic projection, spatial–temporal encoding, motion feature selection, and multi-directional motion integration. Extensive experiments on pigeon neurophysiological experiments and image sequence data showed that the TSOM is biologically interpretable and effective in extracting reliable small object motion features from complex high-altitude backgrounds.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"182 ","pages":"Article 106881"},"PeriodicalIF":6.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolutionary architecture search for generative adversarial networks using an aging mechanism-based strategy 使用基于老化机制的策略为生成式对抗网络寻找进化架构

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-09 DOI: 10.1016/j.neunet.2024.106877

Wenxing Man, Liming Xu, Chunlin He

{"title":"Evolutionary architecture search for generative adversarial networks using an aging mechanism-based strategy","authors":"Wenxing Man, Liming Xu, Chunlin He","doi":"10.1016/j.neunet.2024.106877","DOIUrl":"10.1016/j.neunet.2024.106877","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) have emerged as a key technology in artificial intelligence, especially in image generation. However, traditionally hand-designed GAN architectures often face significant training stability challenges, which are effectively addressed by our Evolutionary Neural Architecture Search (ENAS) algorithm for GANs, named EAMGAN. This one-shot model automates the design of GAN architectures and employs an Operation Importance Metric (OIM) to enhance training stability. It also incorporates an aging mechanism to optimize the selection process during architecture search. Additionally, the use of a non-dominated sorting algorithm ensures the generation of Pareto-optimal solutions, promoting diversity and preventing premature convergence. We evaluated our method on benchmark datasets, and the results demonstrate that EAMGAN is highly competitive in terms of efficiency and performance. Our method identified an architecture achieving Inception Scores (IS) of 8.83±0.13 and Fréchet Inception Distance (FID) of 9.55 on CIFAR-10 with only 0.66 GPU days. Results on the STL-10, CIFAR-100, and ImageNet32 datasets further demonstrate the robust portability of our architecture.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"Article 106877"},"PeriodicalIF":6.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A bio-inspired visual collision detection network integrated with dynamic temporal variance feedback regulated by scalable functional countering jitter streaming 由生物启发的视觉碰撞检测网络，集成了由可扩展功能性反抖动流调节的动态时差反馈。

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-08 DOI: 10.1016/j.neunet.2024.106882

Zefang Chang , Hao Chen , Mu Hua , Qinbing Fu , Jigen Peng

{"title":"A bio-inspired visual collision detection network integrated with dynamic temporal variance feedback regulated by scalable functional countering jitter streaming","authors":"Zefang Chang , Hao Chen , Mu Hua , Qinbing Fu , Jigen Peng","doi":"10.1016/j.neunet.2024.106882","DOIUrl":"10.1016/j.neunet.2024.106882","url":null,"abstract":"<div><div>In pursuing artificial intelligence for efficient collision avoidance in robots, researchers draw inspiration from the locust’s visual looming-sensitive neural circuit to establish an efficient neural network for collision detection. However, existing bio-inspired collision detection neural networks encounter challenges posed by jitter streaming, a phenomenon commonly experienced, for example, when a ground robot moves across uneven terrain. Visual inputs from jitter streaming induce significant fluctuations in grey values, distracting existing bio-inspired networks from extracting visually looming features. To overcome this limitation, we derive inspiration from the potential of feedback loops to enable the brain to generate a coherent visual perception. We introduce a novel dynamic temporal variance feedback loop regulated by scalable functional into the traditional bio-inspired collision detection neural network. This feedback mechanism extracts dynamic temporal variance information from the output of higher-order neurons in the conventional network to assess the fluctuation level of local neural responses and regulate it by a scalable functional to differentiate variance induced by incoherent visual input. Then the regulated signal is reintegrated into the input through negative feedback loop to reduce the incoherence of the signal within the network. Numerical experiments substantiate the effectiveness of the proposed feedback loop in promoting collision detection against jitter streaming. This study extends the capabilities of bio-inspired collision detection neural networks to address jitter streaming challenges, offering a novel insight into the potential of feedback mechanisms in enhancing visual neural abilities.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"182 ","pages":"Article 106882"},"PeriodicalIF":6.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dopamine-induced relaxation of spike synchrony diversifies burst patterns in cultured hippocampal networks 多巴胺诱导的尖峰同步松弛使培养海马网络中的突发性模式多样化

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-07 DOI: 10.1016/j.neunet.2024.106888

Huu Hoang , Nobuyoshi Matsumoto , Miyuki Miyano , Yuji Ikegaya , Aurelio Cortese

引用次数: 0

Barrier-critic-disturbance approximate optimal control of nonzero-sum differential games for modular robot manipulators 模块化机器人操纵器的非零和微分博弈的障碍批判-扰动近似最优控制。

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-06 DOI: 10.1016/j.neunet.2024.106880

Bo Dong, Xinye Zhu, Tianjiao An, Hucheng Jiang, Bing Ma

{"title":"Barrier-critic-disturbance approximate optimal control of nonzero-sum differential games for modular robot manipulators","authors":"Bo Dong, Xinye Zhu, Tianjiao An, Hucheng Jiang, Bing Ma","doi":"10.1016/j.neunet.2024.106880","DOIUrl":"10.1016/j.neunet.2024.106880","url":null,"abstract":"<div><div>In this paper, for addressing the safe control problem of modular robot manipulators (MRMs) system with uncertain disturbances, an approximate optimal control scheme of nonzero-sum (NZS) differential games is proposed based on the control barrier function (CBF). The dynamic model of the manipulator system integrates joint subsystems through the utilization of joint torque feedback (JTF) technique, incorporating interconnected dynamic coupling (IDC) effects. By integrating the cost functions relevant to each player with the CBF, the evolution of system states is ensured to remain within the safe region. Subsequently, the optimal tracking control problem for the MRM system is reformulated as an NZS game involving multiple joint subsystems. Based on the adaptive dynamic programming (ADP) algorithm, a cost function approximator for solving Hamilton–Jacobi (HJ) equation using only critic neural networks (NN) is established, which promotes the feasible derivation of the approximate optimal control strategy. The Lyapunov theory is utilized to demonstrate that the tracking error is uniformly ultimately bounded (UUB). Utilizing the CBF’s state constraint mechanism prevents the robot from deviating from the safe region, and the application of the NZS game approach ensures that the subsystems of the MRM reach a Nash equilibrium. The proposed control method effectively addresses the problem of safe and approximate optimal control of MRM system under uncertain disturbances. Finally, the effectiveness and superiority of the proposed method are verified through simulations and experiments.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"Article 106880"},"PeriodicalIF":6.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rigid propagation of visual motion in the insect’s neural system 视觉运动在昆虫神经系统中的刚性传播

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-06 DOI: 10.1016/j.neunet.2024.106874

Hao Chen , Boquan Fan , Haiyang Li , Jigen Peng

{"title":"Rigid propagation of visual motion in the insect’s neural system","authors":"Hao Chen , Boquan Fan , Haiyang Li , Jigen Peng","doi":"10.1016/j.neunet.2024.106874","DOIUrl":"10.1016/j.neunet.2024.106874","url":null,"abstract":"<div><div>In the pursuit of developing an efficient artificial visual system for visual motion detection, researchers find inspiration from the visual motion-sensitive neural pathways in the insect’s neural system. Although multiple proposed neural computational models exhibit significant performance aligned with those observed from insects, the mathematical basis for how these models characterize the sensitivity of visual neurons to corresponding motion patterns remains to be elucidated. To fill this research gap, this study originally proposed that the rigid propagation of visual motion is an essential mathematical property of the models for the insect’s visual neural system, meaning that the dynamics of the model output remain consistent with the visual motion dynamics reflected in the input. To verify this property, this study uses the small target motion detector (STMD) neural pathway — one of the visual motion-sensitive pathways in the insect’s neural system — as an exemplar, rigorously demonstrating that the dynamics of translational visual motion are rigidly propagated through the encoding of retinal measurements in STMD computational models. Numerical experiment results further substantiate the proposed property of STMD models. This study offers a novel theoretical framework for exploring the nature of the visual motion perception underlying the insect’s visual neural system and brings an innovative perspective to the broader research field of insect visual motion perception and artificial visual systems.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"Article 106874"},"PeriodicalIF":6.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AmbiBias Contrast: Enhancing debiasing networks via disentangled space from ambiguity-bias clusters AmbiBias 对比：通过来自模糊偏置群组的分离空间来增强去噪网络。

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-05 DOI: 10.1016/j.neunet.2024.106857

Suneung Kim, Seong-Whan Lee

{"title":"AmbiBias Contrast: Enhancing debiasing networks via disentangled space from ambiguity-bias clusters","authors":"Suneung Kim, Seong-Whan Lee","doi":"10.1016/j.neunet.2024.106857","DOIUrl":"10.1016/j.neunet.2024.106857","url":null,"abstract":"<div><div>The goal of debiasing in classification tasks is to train models to be less sensitive to correlations between a sample’s target attribution and periodically occurring contextual attributes to achieve accurate classification. A prevalent method involves applying re-weighing techniques to lower the weight of bias-aligned samples that contribute to bias, thereby focusing the training on bias-conflicting samples that deviate from the bias patterns. Our empirical analysis indicates that this approach is effective in datasets where bias-conflicting samples constitute a minority compared to bias-aligned samples, yet its effectiveness diminishes in datasets with similar proportions of both. This ineffectiveness in varied dataset compositions suggests that the traditional method cannot be practical in diverse environments as it overlooks the dynamic nature of dataset-induced biases. To address this issue, we introduce a contrastive approach named “AmbiBias Contrast”, which is robust across various dataset compositions. This method accounts for “ambiguity bias”— the variable nature of bias elements across datasets, which cannot be clearly defined. Given the challenge of defining bias due to the fluctuating compositions of datasets, we designed a method of representation learning that accommodates this ambiguity. Our experiments across a range of and dataset configurations verify the robustness of our method, delivering state-of-the-art performance.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"Article 106857"},"PeriodicalIF":6.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A unified noise and watermark removal from information bottleneck-based modeling 从基于信息瓶颈的建模中统一去除噪声和水印。

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-04 DOI: 10.1016/j.neunet.2024.106853

Hanjuan Huang , Hsing-Kuo Pao

{"title":"A unified noise and watermark removal from information bottleneck-based modeling","authors":"Hanjuan Huang , Hsing-Kuo Pao","doi":"10.1016/j.neunet.2024.106853","DOIUrl":"10.1016/j.neunet.2024.106853","url":null,"abstract":"<div><div>Both image denoising and watermark removal aim to restore a clean image from an observed noisy or watermarked one. The past research consists of the non-learning type with limited effectiveness or the learning types with limited interpretability. To address these issues simultaneously, we propose a method to deal with both the image-denoising and watermark removal tasks in a unified approach. The noises and watermarks are both considered to have different nuisance patterns from the original image content, therefore should be detected by robust image analysis. The unified detection method is based on the well-known information bottleneck (IB) theory and the proposed SIB-GAN where image content and nuisance patterns are well separated by a supervised approach. The IB theory guides us to keep the valuable content such as the original image by a controlled compression on the input (the noisy or watermark-included image) and then only the content without the nuisances can go through the network for effective noise or watermark removal. Additionally, we adjust the compression parameter in IB theory to learn a representation that approaches the minimal sufficient representation of the image content. In particular, to deal with the non-blind noises, an appropriate amount of compression can be estimated from the solid theory foundation. Working on the denoising task given the unseen data with blind noises also shows the model’s generalization power. All of the above shows the interpretability of the proposed method. Overall, the proposed method has achieved promising results across three tasks: image denoising, watermark removal, and mixed noise and watermark removal, obtaining resultant images very close to the original image content and owning superior performance to almost all state-of-the-art approaches that deal with the same tasks.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"Article 106853"},"PeriodicalIF":6.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedART: A neural model integrating federated learning and adaptive resonance theory FedART：融合了联合学习和自适应共振理论的神经模型。

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-04 DOI: 10.1016/j.neunet.2024.106845

Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

{"title":"FedART: A neural model integrating federated learning and adaptive resonance theory","authors":"Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan","doi":"10.1016/j.neunet.2024.106845","DOIUrl":"10.1016/j.neunet.2024.106845","url":null,"abstract":"<div><div>Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed clients while preserving data privacy. However, prevailing FL approaches aggregate the clients’ local models into a global model through multi-round iterative parameter averaging. This leads to the undesirable bias of the aggregated model towards certain clients in the presence of heterogeneous data distributions among the clients. Moreover, such approaches are restricted to supervised classification tasks and do not support unsupervised clustering. To address these limitations, we propose a novel one-shot FL approach called Federated Adaptive Resonance Theory (FedART) which leverages self-organizing Adaptive Resonance Theory (ART) models to learn category codes, where each code represents a cluster of similar data samples. In FedART, the clients learn to associate their private data with various local category codes. Under heterogeneity, the local codes across different clients represent heterogeneous data. In turn, a global model takes these local codes as inputs and aggregates them into global category codes, wherein heterogeneous client data is indirectly represented by distinctly encoded global codes, in contrast to the averaging out of parameters in the existing approaches. This enables the learned global model to handle heterogeneous data. In addition, FedART employs a universal learning mechanism to support both federated classification and clustering tasks. Our experiments conducted on various federated classification and clustering tasks show that FedART consistently outperforms state-of-the-art FL methods on data with heterogeneous distribution across clients.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"Article 106845"},"PeriodicalIF":6.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global practical finite-time synchronization of disturbed inertial neural networks by delayed impulsive control 通过延迟脉冲控制实现受干扰惯性神经网络的全球实用有限时间同步

IF 6 1区计算机科学

Neural Networks Pub Date : 2024-11-04 DOI: 10.1016/j.neunet.2024.106873

Qian Cui , Jinde Cao , Mahmoud Abdel-Aty , Ardak Kashkynbayev

{"title":"Global practical finite-time synchronization of disturbed inertial neural networks by delayed impulsive control","authors":"Qian Cui , Jinde Cao , Mahmoud Abdel-Aty , Ardak Kashkynbayev","doi":"10.1016/j.neunet.2024.106873","DOIUrl":"10.1016/j.neunet.2024.106873","url":null,"abstract":"<div><div>This paper delves into the practical finite-time synchronization (FTS) problem for inertial neural networks (INNs) with external disturbances. Firstly, based on Lyapunov theory, the local practical FTS of INNs with bounded external disturbances can be realized by effective finite time control. Then, building upon the local results, we extend the synchronization to a global practical level under delayed impulsive control. By designing appropriate hybrid controllers, the global practical FTS criteria of disturbed INNs are obtained and the corresponding settling time is estimated. In addition, for impulsive control, the maximum impulsive interval is used to describe the frequency at which the impulses occur. We optimize the maximum impulsive interval, aiming to minimize impulses occurrence, which directly translates to reduced control costs. Moreover, by comparing the global FTS results for INNs without external disturbances, it can be found that the existence of perturbations necessitates either higher impulsive intensity or denser impulses to maintain networks synchronization. Two examples are shown to demonstrate the reasonableness of designed hybrid controllers.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"Article 106873"},"PeriodicalIF":6.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0