Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.8
2.7
Journals
Brain Sciences
Special Issues
Neural Network in Computational Neuroscience
share announcement

Neural Network in Computational Neuroscience

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 2553

Special Issue Editor

Dr. Ahmed Elazab

E-Mail Website
Guest Editor
Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
Interests: medical image analysis; magnetic resonance imaging; pattern recognition; neuroscience; machine learning; deep learning; computer-aided diagnosis and detection

Special Issue Information

Dear Colleagues,

Neural Networks in Computational Neuroscience is a rapidly advancing field that intersects the study of biological neural networks and the artificial neural networks employed in machine learning and artificial intelligence. This interdisciplinary approach aims to understand how the brain computes and processes information, using insights from both neuroscience and computer science. Understanding the similarities and differences between biological neural networks (like those in the brain) and artificial neural networks is of great significance in the understanding, detection, and early diagnosis of various brain diseases. Developing computational models to simulate neural processes is vital, as it helps to understand how neurons and neural circuits process information. This Special Issue aims to publish novel methodologies and applications related to the above concepts. The scope of this Special Issue includes, but is not limited to, the following topics:

  1. Developing bio-inspired neural network models that more closely mimic the structure and function of biological neural networks, potentially leading to more efficient and robust AI systems.
  2. Understanding brain–computer interfaces and how neural networks can be utilized to improve BCIs, which could have profound implications for medicine, prosthetics, and human–computer interaction.
  3. Neural coding and information processing: exploring how the brain encodes information, with potential applications in improving the processing of AI systems process and their interpretation of data.
  4. Using neural networks to simulate brain disorders, leading to an enhanced understanding of these conditions and potential treatments.
  5. Novel methods to better understand neurodegenerative disease, tumors, ischemic stroke, etc.
  6. Using multimodal imaging and electrophysiology data for effective disease diagnosis and detection.
  7. Other related topics.

Dr. Ahmed Elazab
Guest Editor

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. Brain Sciences is an international peer-reviewed open access monthly 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 2200 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

  • neural networks
  • computational neuroscience
  • artificial intelligence
  • machine learning
  • brain-computer interface
  • information processing
  • neural coding
  • brain disorders
  • novel methods
  • brain imaging and electrophysiology

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

13 pages, 2533 KiB  
Article
Intracerebral Hemorrhage Prognosis Classification via Joint-Attention Cross-Modal Network
by Manli Xu, Xianjun Fu, Hui Jin, Xinlei Yu, Gang Xu, Zishuo Ma, Cheng Pan and Bo Liu
Brain Sci. 2024, 14(6), 618; https://doi.org/10.3390/brainsci14060618 - 20 Jun 2024
Viewed by 750
Abstract
Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term [...] Read more.
Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term disability associated with ICH. Due to the complexity of ICH, the diagnosis of ICH in clinical practice heavily relies on the professional expertise and clinical experience of physicians. Traditional prognostic methods largely depend on the specialized knowledge and subjective judgment of healthcare professionals. Meanwhile, existing artificial intelligence (AI) methodologies, which predominantly utilize features derived from computed tomography (CT) scans, fall short of capturing the multifaceted nature of ICH. Although existing methods are capable of integrating clinical information and CT images for prognosis, the effectiveness of this fusion process still requires improvement. To surmount these limitations, the present study introduces a novel AI framework, termed the ICH Network (ICH-Net), which employs a joint-attention cross-modal network to synergize clinical textual data with CT imaging features. The architecture of ICH-Net consists of three integral components: the Feature Extraction Module, which processes and abstracts salient characteristics from the clinical and imaging data, the Feature Fusion Module, which amalgamates the diverse data streams, and the Classification Module, which interprets the fused features to deliver prognostic predictions. Our evaluation, conducted through a rigorous five-fold cross-validation process, demonstrates that ICH-Net achieves a commendable accuracy of up to 87.77%, outperforming other state-of-the-art methods detailed within our research. This evidence underscores the potential of ICH-Net as a formidable tool in prognosticating ICH, promising a significant advancement in clinical decision-making and patient care. Full article
(This article belongs to the Special Issue Neural Network in Computational Neuroscience)
►▼ Show Figures

Figure 1

28 pages, 5849 KiB  
Article
TSANN-TG: Temporal–Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition
by Chao Jiang, Yingying Dai, Yunheng Ding, Xi Chen, Yingjie Li and Yingying Tang
Brain Sci. 2024, 14(5), 516; https://doi.org/10.3390/brainsci14050516 - 20 May 2024
Cited by 1 | Viewed by 1122
Abstract
Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the realm of affective brain–computer interfaces. In this paper, we propose TSANN-TG (temporal–spatial attention neural network with a task-specific graph), a novel neural network architecture tailored for enhancing feature extraction and effectively integrating temporal–spatial features. [...] Read more.
Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the realm of affective brain–computer interfaces. In this paper, we propose TSANN-TG (temporal–spatial attention neural network with a task-specific graph), a novel neural network architecture tailored for enhancing feature extraction and effectively integrating temporal–spatial features. TSANN-TG comprises three primary components: a node-feature-encoding-and-adjacency-matrices-construction block, a graph-aggregation block, and a graph-feature-fusion-and-classification block. Leveraging the distinct temporal scales of features from EEG signals, TSANN-TG incorporates attention mechanisms for efficient feature extraction. By constructing task-specific adjacency matrices, the graph convolutional network with an attention mechanism captures the dynamic changes in dependency information between EEG channels. Additionally, TSANN-TG emphasizes feature integration at multiple levels, leading to improved performance in emotion-recognition tasks. Our proposed TSANN-TG is applied to both our FTEHD dataset and the publicly available DEAP dataset. Comparative experiments and ablation studies highlight the excellent recognition results achieved. Compared to the baseline algorithms, TSANN-TG demonstrates significant enhancements in accuracy and F1 score on the two benchmark datasets for four types of cognitive tasks. These results underscore the significant potential of the TSANN-TG method to advance EEG-based emotion recognition. Full article
(This article belongs to the Special Issue Neural Network in Computational Neuroscience)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop