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Electronics
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Recent Advances and Related Technologies in Neuromorphic Computing
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Recent Advances and Related Technologies in Neuromorphic Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 6167

Special Issue Editors

Dr. Gang Dou

E-Mail Website
Guest Editor
The College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
Interests: memristor and artificial neural network; intelligent information processing; nonvolatile storage; intelligent bionic sensor
Prof. Dr. Mei Guo

E-Mail Website
Guest Editor
The College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
Interests: memristor and intelligent sensing devices
Dr. Zhixia Ding

E-Mail Website
Guest Editor
The School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: the memristor neuromorphic system

Special Issue Information

Dear Colleagues,

Neuromorphic computing, inspired by biology and mimicking the neural systems of the human brain, promises extraordinary performance and energy efficiency. In addition to this, neuromorphic computing is ideally suited to low-power edge AI applications.

This Special Issue aims to explore the recent advances, challenges, and related technologies in the field of neuromorphic computing. Original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

  • Memristive devices for neuromorphic computing;
  • Dynamics of nonlinear systems;
  • Dynamic memories on memristor-based circuits and systems;
  • Emerging technologies for neuromorphic computing;
  • Computational neuroscience;
  • Mathematical modeling of neural systems;
  • Neurodynamic optimization and adaptive dynamic programming;
  • Embedded neural systems;
  • Hybrid intelligent systems supervised;
  • Robotic and control applications.

Dr. Gang Dou
Prof. Dr. Mei Guo
Dr. Zhixia Ding
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • memristive devices
  • neuromorphic computing
  • nonlinear systems
  • dynamic memories
  • memristor-based circuits
  • mathematical modeling
  • adaptive dynamic programming
  • hybrid intelligent systems
  • robotic applications

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Published Papers (6 papers)

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Research

17 pages, 17113 KiB  
Article
Complex Dynamical Behavior of Locally Active Discrete Memristor-Coupled Neural Networks with Synaptic Crosstalk: Attractor Coexistence and Reentrant Feigenbaum Trees
by Deheng Liu, Kaihua Wang, Yinghong Cao and Jinshi Lu
Electronics 2024, 13(14), 2776; https://doi.org/10.3390/electronics13142776 - 15 Jul 2024
Viewed by 495
Abstract
In continuous neural modeling, memristor coupling has been investigated widely. Yet, there is little research on discrete neural networks in the field. Discrete models with synaptic crosstalk are even less common. In this paper, two locally active discrete memristors are used to couple [...] Read more.
In continuous neural modeling, memristor coupling has been investigated widely. Yet, there is little research on discrete neural networks in the field. Discrete models with synaptic crosstalk are even less common. In this paper, two locally active discrete memristors are used to couple two discrete Aihara neurons to form a map called DMCAN. Then, the synapse is modeled using a discrete memristor and the DMCAN map with crosstalk is constructed. The DMCAN map is investigated using phase diagram, chaotic sequence, Lyapunov exponent spectrum (LEs) and bifurcation diagrams (BD). Its rich and complex dynamical behavior, which includes attractor coexistence, state transfer, Feigenbaum trees, and complexity, is systematically analyzed. In addition, the DMCAN map is implemented in hardware on a DSP platform. Numerical simulations are further validated for correctness. Numerical and experimental findings show that the synaptic connections of neurons can be modeled by discrete memristor coupling which leads to the construction of more complicated discrete neural networks. Full article
(This article belongs to the Special Issue Recent Advances and Related Technologies in Neuromorphic Computing)
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20 pages, 8458 KiB  
Article
Cross-Channel Color Image Encryption Scheme Based on Discrete Memristive Coupled Neurons and DWT Compression
by Yinghong Cao, Chang Cai, Xianying Xu and Xiuguo Bi
Electronics 2024, 13(13), 2647; https://doi.org/10.3390/electronics13132647 - 5 Jul 2024
Cited by 1 | Viewed by 537
Abstract
To address the consumption and security of color images for transmission and storage, a cross-channel color image encryption scheme based on a discrete memristive coupled neuron model and DWT compression is designed in this article. Firstly, the dynamics of the discrete memristive coupled [...] Read more.
To address the consumption and security of color images for transmission and storage, a cross-channel color image encryption scheme based on a discrete memristive coupled neuron model and DWT compression is designed in this article. Firstly, the dynamics of the discrete memristive coupled neuron system are analyzed and found to possess the hyperchaotic phenomenon, which provides sufficient security for the encryption scheme. Secondly, the color image processed by discrete wavelet transform (DWT) has a quarter of the previous capacity. Then, the color image is combined with a Hash function, and the resulting Hash sequence is given the initial value of the hyperchaotic system. Next, a particle swarm foraging algorithm (PSFA) is designed to better disrupt the correlation in the RGB channel. Finally, a complementary DNA coding rule is implemented for the further encryption of color images. Simulation results show that even with DWT lossy compression, the recovered image can be clearly seen. The performance analysis illustrates that under the hyperchaotic system, the proposed encryption algorithm brings higher security for color images. Full article
(This article belongs to the Special Issue Recent Advances and Related Technologies in Neuromorphic Computing)
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29 pages, 63676 KiB  
Article
Color Image Encryption Based on a Novel Fourth-Direction Hyperchaotic System
by Zhuoyi Lei, Jiacheng Yang, Hanshuo Qiu, Xiangzi Zhang and Jizhao Liu
Electronics 2024, 13(12), 2229; https://doi.org/10.3390/electronics13122229 - 7 Jun 2024
Cited by 1 | Viewed by 776
Abstract
Neuromorphic computing draws inspiration from the brain to design energy-efficient hardware for information processing, enabling highly complex tasks. In neuromorphic computing, chaotic phenomena describe the nonlinear interactions and dynamic behaviors. Chaotic behavior can be utilized in neuromorphic computing to accomplish complex information processing [...] Read more.
Neuromorphic computing draws inspiration from the brain to design energy-efficient hardware for information processing, enabling highly complex tasks. In neuromorphic computing, chaotic phenomena describe the nonlinear interactions and dynamic behaviors. Chaotic behavior can be utilized in neuromorphic computing to accomplish complex information processing tasks; therefore, studying chaos is crucial. Today, more and more color images are appearing online. However, the generation of numerous images has also brought about a series of security issues. Ensuring the security of images is crucial. We propose a novel fourth-direction hyperchaotic system in this paper. In comparison to low-dimensional chaotic systems, the proposed hyperchaotic system exhibits a higher degree of unpredictability and various dynamic behaviors. The dynamic behaviors include fourth-direction hyperchaos, third-direction hyperchaos, and second-direction hyperchaos. The hyperchaotic system generates chaotic sequences. These chaotic sequences are the foundation of the encryption scheme discussed in this paper. Images are altered by employing methods such as row and column scrambling as well as diffusion. These operations will alter both the pixel values and positions. The proposed encryption scheme has been analyzed through security and application scenario analyses. We perform a security analysis to evaluate the robustness and weaknesses of the encryption scheme. Moreover, we conduct an application scenario analysis to help determine the practical usability and effectiveness of the encryption scheme in real-world situations. These analyses demonstrate the efficiency of the encryption scheme. Full article
(This article belongs to the Special Issue Recent Advances and Related Technologies in Neuromorphic Computing)
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12 pages, 1186 KiB  
Article
Optimal Transport-Embedded Neural Network for Fairness Transfer Problem
by Muchao Xiang, Zaixun Ling, Qine Liu and Yaoxuan Zhang
Electronics 2023, 12(21), 4481; https://doi.org/10.3390/electronics12214481 - 31 Oct 2023
Viewed by 921
Abstract
Research on neuromorphic computing has gained popularity in recent years. In particular, regularized embedded neural systems have been applied in several significant real-world situations, such as recommendation systems and transfer learning. This paper deals with the fairness transfer learning problem, which has been [...] Read more.
Research on neuromorphic computing has gained popularity in recent years. In particular, regularized embedded neural systems have been applied in several significant real-world situations, such as recommendation systems and transfer learning. This paper deals with the fairness transfer learning problem, which has been insufficiently explored. In fairness transfer settings, the source domain has limit-tagged training samples, which may lead to performance degradation in the target domain. To solve such problems, a linear data-augmentation-based optimal transport-embedded neural network is proposed in this paper. It can augment the source samples to make the distribution of the source domain balanced and can align the source and target distributions simultaneously. Moreover, the distribution of the augmented data by mixup is limited to a certain bound that can avoid the abnormal samples generated. The effectiveness of the proposed method has been demonstrated in several transfer learning tests, including regression and classification. In 1-shot and 3-shot classification tasks on the Office dataset, our method’s accuracy is 4.8 and 3.9% better, respectively, than the second-best model. Additionally, our model’s performance is about 2–3 percentage points superior to the second-best model in the OfficeHome dataset. It is simple yet effective, making it perfect for low-power edge AI applications. Full article
(This article belongs to the Special Issue Recent Advances and Related Technologies in Neuromorphic Computing)
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13 pages, 3549 KiB  
Article
CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion
by Jun Lu, Xiaokai Ren, Jiaxin Zhang and Ting Wang
Electronics 2023, 12(19), 4025; https://doi.org/10.3390/electronics12194025 - 25 Sep 2023
Cited by 3 | Viewed by 1436
Abstract
Malware is a significant threat to the field of cyber security. There is a wide variety of malware, which can be programmed to threaten computer security by exploiting various networks, operating systems, software and physical security vulnerabilities. So, detecting malware has become a [...] Read more.
Malware is a significant threat to the field of cyber security. There is a wide variety of malware, which can be programmed to threaten computer security by exploiting various networks, operating systems, software and physical security vulnerabilities. So, detecting malware has become a significant part of maintaining network security. In this paper, data enhancement techniques are used in the data preprocessing stage, then a novel detection mode—CPL-Net employing malware texture image—is proposed. The model consists of a feature extraction component, a feature fusion component and a classification component, the core of which is based on the parallel fusion of spatio-temporal features by Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). Through experiments, it has been proven that CPL-Net can achieve an accuracy of 98.7% and an F1 score of 98.6% for malware. The model uses a novel feature fusion approach and achieves a comprehensive and precise malware detection. Full article
(This article belongs to the Special Issue Recent Advances and Related Technologies in Neuromorphic Computing)
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18 pages, 8037 KiB  
Article
Optimal Reconstruction of Single-Pixel Images through Feature Feedback Mechanism and Attention
by Zijun Gao, Jingwen Su, Junjie Zhang, Zhankui Song, Bo Li and Jue Wang
Electronics 2023, 12(18), 3838; https://doi.org/10.3390/electronics12183838 - 11 Sep 2023
Viewed by 995
Abstract
The single-pixel imaging technique can reconstruct high-quality images using only a bucket detector with no spatial resolution, and the image quality is degraded in order to meet the demands of real-time applications. According to some studies of algorithm performance, the network model performs [...] Read more.
The single-pixel imaging technique can reconstruct high-quality images using only a bucket detector with no spatial resolution, and the image quality is degraded in order to meet the demands of real-time applications. According to some studies of algorithm performance, the network model performs differently in simulated and real-world experiments. We propose an end-to-end neural network capable of reconstructing 2D images from experimentally obtained 1D signals optimally. In order to improve the image quality of real-time single-pixel imaging, we built a feedback module in the hidden layer of the recurrent neural network to implement feature feedback. The feedback module fuses high-level features of undersampled images with low-level features through dense jump connections and multi-scale balanced attention modules to gradually optimize the feature extraction process and reconstruct high-quality images. In addition, we introduce a learning strategy that combines mean loss with frequency domain loss to improve the network’s ability to reconstruct complex undersampled images. In this paper, the factors that lead to the degradation of single-pixel imaging are analyzed, and a network degradation model suitable for physical imaging systems is designed. The experiment results indicate that the reconstructed images utilizing the proposed method have better quality metrics and visual effects than the excellent methods in the field of single-pixel imaging. Full article
(This article belongs to the Special Issue Recent Advances and Related Technologies in Neuromorphic Computing)
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