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Biomimetics
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Biomimetics and Bioinspired Artificial Intelligence Applications: 2nd Edition
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Biomimetics and Bioinspired Artificial Intelligence Applications: 2nd Edition

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 May 2025 | Viewed by 2897

Special Issue Editors

Prof. Dr. Chaoran Cui

E-Mail Website
Guest Editor
School of Computer Science and Technology, Shandong University of Finance and Economics, No. 7366, East Second Ring Road, Yaojia Sub-District, Jinan 250014, China
Interests: machine learning; data mining; multimedia processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaohui Han

E-Mail Website
Guest Editor
Shandong Computer Science Center (National Supercomputer Center in Jinan), Shandong Provincial Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
Interests: machine learning; data mining; multimedia processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomimetics focuses on living systems and attempts to transfer their properties to engineering applications, dramatically influencing human civilization. In recent decades, the integration of biomimetics and computing methods has achieved great results in a variety of artificial intelligence applications, including medical diagnosis, robotics, optimization, and pattern recognition. This Special Issue seeks to understand how to design biomimetic machinery and material models that mimic the properties and structures of organisms and report the latest advances in bioinspired algorithms in artificial intelligence. We welcome original research, meta-analysis, and review articles covering (but not limited to) the following potential topics:

  • Biomimetics of materials and structures;
  • Biomimetic design, construction, and devices;
  • Bioinspired robotics and autonomous systems;
  • Applications of bioinspired methods in computer vision and signal processing;
  • Brain-inspired computing methods, e.g., neural networks and deep learning;
  • Swarm intelligence and collective behaviour, g., particle swarm optimization and ant colony optimization;
  • Evolutionary algorithms and optimization, e.g., genetic algorithms;
  • Adaptive and self-learning systems.

Prof. Dr. Chaoran Cui
Dr. Xiaohui Han
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

  • biomimetics of materials and structures
  • biomimetic design, construction, and devices
  • bioinspired robotics and autonomous systems
  • applications of bioinspired methods in computer vision and signal processing
  • brain-inspired computing methods, e.g., neural networks and deep learning
  • swarm intelligence and collective behaviour, e.g., particle swarm optimization and ant colony optimization
  • evolutionary algorithms and optimization, e.g., genetic algorithms
  • adaptive and self-learning systems

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Related Special Issue

Published Papers (3 papers)

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16 pages, 1693 KiB  
Article
A Gaussian Mixture Model-Based Unsupervised Dendritic Artificial Visual System for Motion Direction Detection
by Zhiyu Qiu, Yuxiao Hua, Tianqi Chen, Yuki Todo, Zheng Tang, Delai Qiu and Chunping Chu
Biomimetics 2025, 10(5), 332; https://doi.org/10.3390/biomimetics10050332 - 19 May 2025
Viewed by 137
Abstract
Motion perception is a fundamental function of biological visual systems, enabling organisms to navigate dynamic environments, detect threats, and track moving objects. Inspired by the mechanisms of biological motion processing, we propose an Unsupervised Artificial Visual System for motion direction detection. Unlike traditional [...] Read more.
Motion perception is a fundamental function of biological visual systems, enabling organisms to navigate dynamic environments, detect threats, and track moving objects. Inspired by the mechanisms of biological motion processing, we propose an Unsupervised Artificial Visual System for motion direction detection. Unlike traditional supervised learning approaches, our model employs unsupervised learning to classify local motion direction detection neurons and group those with similar directional preferences to form macroscopic motion direction detection neurons. The activation of these neurons is proportional to the received input, and the neuron with the highest activation determines the macroscopic motion direction of the object. The proposed system consists of two layers: a local motion direction detection layer and an unsupervised global motion direction detection layer. For local motion detection, we adopt the Local Motion Detection Neuron (LMDN) model proposed in our previous work, which detects motion in eight different directions. The outputs of these neurons serve as inputs to the global motion direction detection layer, which employs a Gaussian Mixture Model (GMM) for unsupervised clustering. GMM, a probabilistic clustering method, effectively classifies local motion detection neurons according to their preferred directions, aligning with biological principles of sensory adaptation and probabilistic neural processing. Through repeated exposure to motion stimuli, our model self-organizes to detect macroscopic motion direction without the need for labeled data. Experimental results demonstrate that the GMM-based global motion detection layer successfully classifies motion direction signals, forming structured motion representations akin to biological visual systems. Furthermore, the system achieves motion direction detection accuracy comparable to previous supervised models while offering a more biologically plausible mechanism. This work highlights the potential of unsupervised learning in artificial vision and contributes to the development of adaptive motion perception models inspired by neural computation. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications: 2nd Edition)
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31 pages, 6822 KiB  
Article
MHO: A Modified Hippopotamus Optimization Algorithm for Global Optimization and Engineering Design Problems
by Tao Han, Haiyan Wang, Tingting Li, Quanzeng Liu and Yourui Huang
Biomimetics 2025, 10(2), 90; https://doi.org/10.3390/biomimetics10020090 - 5 Feb 2025
Viewed by 1325
Abstract
The hippopotamus optimization algorithm (HO) is a novel metaheuristic algorithm that solves optimization problems by simulating the behavior of hippopotamuses. However, the traditional HO algorithm may encounter performance degradation and fall into local optima when dealing with complex global optimization and engineering design [...] Read more.
The hippopotamus optimization algorithm (HO) is a novel metaheuristic algorithm that solves optimization problems by simulating the behavior of hippopotamuses. However, the traditional HO algorithm may encounter performance degradation and fall into local optima when dealing with complex global optimization and engineering design problems. In order to solve these problems, this paper proposes a modified hippopotamus optimization algorithm (MHO) to enhance the convergence speed and solution accuracy of the HO algorithm by introducing a sine chaotic map to initialize the population, changing the convergence factor in the growth mechanism, and incorporating the small-hole imaging reverse learning strategy. The MHO algorithm is tested on 23 benchmark functions and successfully solves three engineering design problems. According to the experimental data, the MHO algorithm obtains optimal performance on 13 of these functions and three design problems, exits the local optimum faster, and has better ordering and stability than the other nine metaheuristics. This study proposes the MHO algorithm, which offers fresh insights into practical engineering problems and parameter optimization. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications: 2nd Edition)
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19 pages, 954 KiB  
Article
Memory–Non-Linearity Trade-Off in Distance-Based Delay Networks
by Stefan Iacob and Joni Dambre
Biomimetics 2024, 9(12), 755; https://doi.org/10.3390/biomimetics9120755 - 11 Dec 2024
Viewed by 1040
Abstract
The performance of echo state networks (ESNs) in temporal pattern learning tasks depends both on their memory capacity (MC) and their non-linear processing. It has been shown that linear memory capacity is maximized when ESN neurons have linear activation, and that a trade-off [...] Read more.
The performance of echo state networks (ESNs) in temporal pattern learning tasks depends both on their memory capacity (MC) and their non-linear processing. It has been shown that linear memory capacity is maximized when ESN neurons have linear activation, and that a trade-off between non-linearity and linear memory capacity is required for temporal pattern learning tasks. The more recent distance-based delay networks (DDNs) have shown improved memory capacity over ESNs in several benchmark temporal pattern learning tasks. However, it has not thus far been studied whether this increased memory capacity comes at the cost of reduced non-linear processing. In this paper, we advance the hypothesis that DDNs in fact achieve a better trade-off between linear MC and non-linearity than ESNs, by showing that DDNs can have strong non-linearity with large memory spans. We tested this hypothesis using the NARMA-30 task and the bitwise delayed XOR task, two commonly used reservoir benchmark tasks that require a high degree of both non-linearity and memory. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications: 2nd Edition)
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