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Biomimetics
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Dendritic Neuron Model: Theory, Design, Optimization and Applications
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Dendritic Neuron Model: Theory, Design, Optimization and Applications

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 709

Special Issue Editors

Dr. Junkai Ji

E-Mail Website
Guest Editor
National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
Interests: AI for drug design; neural networks; evolutionary computation
Prof. Dr. Zheng Tang

E-Mail Website
Guest Editor
Institute of AI for Industries, Chinese Academy of Sciences, 168 Tianquan Road, Nanjing, China
Interests: intellectual information technology; neural networks; optimizations
Dr. Yuki Todo

E-Mail Website
Guest Editor
Faculty of Electrical and Computer Engineering, Kanazawa University, Kanazawa-shi 920-1192, Japan
Interests: multiple-valued logic; neural networks; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Different from conventional artificial neural networks, the dendritic neuron model (DNM) has four layers, including the synaptic, dendritic, membrane, and soma layers. With the plastic mechanism, the synapses can adaptively adjust their parameters, and then the dendrites can respond nonlinearly to activation or inhibition according to the inputs of the synapses. In practice, the DNM can be trained to utilize specific functions to prune superfluous synapses and use multiplication operations to prune useless dendritic branches to develop one-of-a-kind morphology for different tasks. In recent years, research into the DNM has attracted considerable attention, and significant advancements have been made in a range of engineering fields, such as pattern recognition, medical diagnosis, stock price prediction and wind speed forecasting, etc. This Special Issue aims to focus on the comprehensive investigation of the DNM and its real-world applications. It is expected that new design and optimization methods will be established for the DNM and its variations. Submissions based on the analysis, design, optimization, and applications of the DNM are welcome. More details can be found at https://jijunkai123.github.io/DNM/index.html.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Theory analysis of the dendritic neuron model;
  • Optimization of the dendritic neuron model;
  • Dynamics of the dendritic neuron model;
  • Design of the dendritic neuron model;
  • Implementation of the dendritic neuron model;
  • Complex-valued dendritic neuron model;
  • Real-world applications of the dendritic neural networks;
  • Learning algorithms of dendritic neural networks;
  • Other biological plausible neural networks.

Dr. Junkai Ji
Prof. Dr. Zheng Tang
Dr. Yuki Todo
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. Biomimetics 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 network
  • dendrite
  • pattern recognition
  • optimization
  • biologically plausible

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Published Papers (2 papers)

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Research

25 pages, 3203 KiB  
Article
A Bio-Inspired Learning Dendritic Motion Detection Framework with Direction-Selective Horizontal Cells
by Tianqi Chen, Yuki Todo, Zhiyu Qiu, Yuxiao Hua, Hiroki Sugiura and Zheng Tang
Biomimetics 2025, 10(5), 286; https://doi.org/10.3390/biomimetics10050286 - 2 May 2025
Viewed by 180
Abstract
Motion direction detection is an essential task for both computer vision and neuroscience. Inspired by the biological theory of the human visual system, we proposed a learnable horizontal-cell-based dendritic neuron model (HCdM) that captures motion direction with high efficiency while remaining highly robust. [...] Read more.
Motion direction detection is an essential task for both computer vision and neuroscience. Inspired by the biological theory of the human visual system, we proposed a learnable horizontal-cell-based dendritic neuron model (HCdM) that captures motion direction with high efficiency while remaining highly robust. Unlike present deep learning models, which rely on extension of computation and extraction of global features, the HCdM mimics the localized processing of dendritic neurons, enabling efficient motion feature integration. Through synaptic learning that prunes unnecessary parts, our model maintains high accuracy in noised images, particularly against salt-and-pepper noise. Experimental results show that the HCdM reached over 99.5% test accuracy, maintained robust performance under 10% salt-and-pepper noise, and achieved cross-dataset generalization exceeding 80% in certain conditions. Comparisons with state-of-the-art (SOTA) models like vision transformers (ViTs) and convolutional neural networks (CNNs) demonstrate the HCdM’s robustness and efficiency. Additionally, in contrast to previous artificial visual systems (AVSs), our findings suggest that lateral geniculate nucleus (LGN) structures, though present in biological vision, may not be essential for motion direction detection. This insight provides a new direction for bio-inspired computational models. Future research will focus on hybridizing the HCdM with SOTA models that perform well on complex visual scenes to enhance its adaptability. Full article
(This article belongs to the Special Issue Dendritic Neuron Model: Theory, Design, Optimization and Applications)
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20 pages, 3509 KiB  
Article
A Neural Network-Accelerated Approach for Orthopedic Implant Design and Evaluation Through Strain Shielding Analysis
by Ana Isabel Lopes Pais, Jorge Lino Alves and Jorge Belinha
Biomimetics 2025, 10(4), 238; https://doi.org/10.3390/biomimetics10040238 - 13 Apr 2025
Viewed by 342
Abstract
The design of orthopedic implants is a complex challenge, requiring the careful balance of mechanical performance and biological integration to ensure long-term success. This study focuses on the development of a porous femoral stem implant aimed at reducing stiffness and mitigating stress shielding [...] Read more.
The design of orthopedic implants is a complex challenge, requiring the careful balance of mechanical performance and biological integration to ensure long-term success. This study focuses on the development of a porous femoral stem implant aimed at reducing stiffness and mitigating stress shielding effects. To accelerate the design process, neural networks were trained to predict the optimal density distribution of the implant, enabling rapid optimization. Two initial design spaces were evaluated, revealing the necessity of incorporating the femur’s anatomical features into the design process. The trained models achieved a median error near 0 for both conventional and extended design spaces, producing optimized designs in a fraction of the computational time typically required. Finite element analysis (FEA) was employed to assess the mechanical performance of the neural network-generated implants. The results demonstrated that the neural network predictions effectively reduced stress shielding compared to a solid model in 50% of the test cases. While the graded porosity implant design did not show significant differences in stress shielding prevention compared to a uniform porosity design, it was found to be significantly stronger, highlighting its potential for enhanced durability. This work underscores the efficacy of neural network-accelerated design in improving implant development efficiency and performance. Full article
(This article belongs to the Special Issue Dendritic Neuron Model: Theory, Design, Optimization and Applications)
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