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Mathematics
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AI for Brain Science and Brain-Inspired Computing
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AI for Brain Science and Brain-Inspired Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1779

Special Issue Editors

Dr. Zhaofei Yu

E-Mail Website
Guest Editor
School of Artificial Intelligence, National Engineering Research Center of Visual Technology, Peking University, Beijing 100871, China
Interests: brain-inspired computing; machine learning; computer vision
Prof. Dr. Haixian Wang

E-Mail Website
Guest Editor
School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
Interests: neural signal decoding; brain computer interfaces
Dr. Qi Xu

E-Mail Website
Guest Editor
School of Artificial Intelligence, Dalian University of Technology, Dalian 116024, China
Interests: neuromorphic computing; brain science; machine learning

Special Issue Information

Dear Colleagues,

Recently, research into artificial intelligence and brain science have promoted and inspired each other. On the one hand, researchers have utilized artificial intelligence techniques, including deep learning and machine learning, to delve into the intricacies of brain information processing, thus enhancing our understanding of its underlying mechanisms. Conversely, insights gained from the study of the human brain's computational principles have paved the way for advancements in artificial intelligence, addressing the challenges faced by the field and propelling its technological advancement.

This particular Topic aims to bring together esteemed scientists working in the domains of artificial intelligence, brain science, and related fields such as machine learning, computer vision, and neuromorphic computing. We eagerly invite scholars to submit original research papers, experimental papers, and dataset papers that are closely aligned with the theme of this Topic. Contributions employing advanced mathematical methods and novel approaches within these areas are especially encouraged for publication.

Dr. Zhaofei Yu
Prof. Dr. Haixian Wang
Dr. Qi Xu
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. Mathematics 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 2600 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

  • machine learning
  • brain science
  • neuromorphic computing
  • deep learning
  • computer vision and image processing
  • evolutionary computation
  • medical image processing

Published Papers (2 papers)

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Research

21 pages, 491 KiB  
Article
A Novel Gaussian Process Surrogate Model with Expected Prediction Error for Optimization under Constraints
by Hongri Cong, Bo Wang and Zhe Wang
Mathematics 2024, 12(7), 1115; https://doi.org/10.3390/math12071115 - 08 Apr 2024
Viewed by 406
Abstract
Optimization, particularly constrained optimization problems (COPs), is fundamental in engineering, influencing various sectors with its critical role in enhancing design efficiency, reducing experimental costs, and shortening testing cycles. This study explores the challenges inherent in COPs, with a focus on developing efficient solution [...] Read more.
Optimization, particularly constrained optimization problems (COPs), is fundamental in engineering, influencing various sectors with its critical role in enhancing design efficiency, reducing experimental costs, and shortening testing cycles. This study explores the challenges inherent in COPs, with a focus on developing efficient solution methodologies under stringent constraints. Surrogate models, especially Gaussian Process Regression (GPR), are pivotal in our approach, enabling the approximation of complex systems with reduced computational demand. We evaluate the efficacy of the Efficient Global Optimization (EGO) algorithm, which synergizes GPR with the Expected Improvement (EI) function, and further extend this framework to Constrained Expected Improvement (CEI) and our novel methodology Constrained Expected Prediction Error (CEPE). We demonstrate the effectiveness of these methodologies by numerical benchmark simulations and the real-world application of optimizing a Three-Bar Truss Design. In essence, the innovative CEPE approach promises a potent balance between solution accuracy and computational prowess, offering significant potential in the broader engineering field. Full article
(This article belongs to the Special Issue AI for Brain Science and Brain-Inspired Computing)
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12 pages, 3286 KiB  
Article
Implementation of Physical Reservoir Computing in a TaOx/FTO-Based Memristor Device
by Dongyeol Ju, Junyoung Ahn, Jungwoo Ho, Sungjun Kim and Daewon Chung
Mathematics 2023, 11(20), 4325; https://doi.org/10.3390/math11204325 - 17 Oct 2023
Cited by 1 | Viewed by 913
Abstract
As one of the solutions to overcome the current problems of computing systems, a resistive switching device, the TiN/TaOx/fluorine-doped tin oxide (FTO) stacked device, was fabricated to investigate its capability in embodying neuromorphic computing. The device showed good uniformity during the [...] Read more.
As one of the solutions to overcome the current problems of computing systems, a resistive switching device, the TiN/TaOx/fluorine-doped tin oxide (FTO) stacked device, was fabricated to investigate its capability in embodying neuromorphic computing. The device showed good uniformity during the resistive switching phenomenon under time and cycle-to-cycle dependent switching, which may be due to the oxygen reservoir characteristics of the FTO bottom electrode, storing oxygen ions during resistive switching and enhancing the device property. Based on the uniform switching phenomenon of the TiN/TaOx/FTO device, the pulse applications were performed to seek its ability to mimic the biological brain. It was revealed that the volatile and non-volatile nature of the device can be altered by controlling the pulse stimuli, where strong stimuli result in long-term memory while weak stimuli result in short-term memory. To further investigate the key functions of the biological brain, various learning rules such as paired-pulse facilitation, excitatory postsynaptic current, potentiation and depression, spike-rate dependent plasticity, and spike-time dependent plasticity were tested, with reservoir computing implemented based on the volatile characteristic of the TiN/TaOx/FTO device. Full article
(This article belongs to the Special Issue AI for Brain Science and Brain-Inspired Computing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Continual Learning Combined with Spiking Neural Networks: A Review of Current Research and Future Prospect
Authors: Zhengji Li, Xuming Ran, Yuyuan Gao, Xuanye Fang, Hongming Xu
Affiliation: Dalian University of Technology

Title: PathoShift: Bridging Histopathology Stains from H&E to IHC via Transformer and Color Deconvolution
Authors: Ranran Wang, Shan Jin, Xinyu Hao, Qi Xu, Hongming Xu*
Affiliation: Dalian University of Technology; University of Jyvaskyla

Title: An FPGA Implementation of Bayesian Inference with Spiking Neural Networks
Authors: Lingling An, Haoran Li, Bo Wan, Qifeng Li, Ying Fang*, Jian K. Liu f
Affiliation: Xidian University

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