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Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation

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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: closed (10 February 2026) | Viewed by 7574

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Special Issue Editors

Dr. Sebelan Danishvar

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Guest Editor

College of Engineering, Design, and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
Interests: deep learning and neural networks; bio-inspired and brain-inspired computation; swarm intelligence and multi-agent systems; medical data analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Sobhan Sheykhivand

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Guest Editor

Department of Biomedical Engineering, University of Bonab, Bonab 5551761167, Iran
Interests: brain-computer-interface; deep learning; biomedical signal processing; neural network; artificial intelligence

Special Issue Information

Dear Colleagues,

Recent years have witnessed the growing intersection of biology and artificial intelligence, leading to breakthrough avenues for developing systems inspired by the resilience, flexibility, and efficiency of living organisms. This Special Issue, entitled “Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation,” aims to assemble insightful research investigating the intersection between biological paradigms and computational intelligence.

Motivated by neural architectures, cognitive functions, and biomimetic designs, researchers are developing next-generation models and machines that have improved perception, reasoning, and sensing of the physical world. This Special Issue provides a platform for research bridging theoretical advances, algorithm development, and applications to real-world domains—ranging from neuromorphic computing and spiking neural networks to soft robotics, swarm intelligence, and biohybrid systems.

We particularly welcome contributions that explore the following:

New neural network architectures inspired by biological cognition and learning;
AI systems based on natural sensory and motor systems;
Biomimetic control, perception, or decision-making algorithms;
Robotic, autonomous system, and intelligent material implementation of bio-inspired ideas;
Interdisciplinary results from neuroscience, synthetic biology, and AI.

With this Special Issue, we aim to generate interdisciplinary dialogues that will accelerate innovation at the forefront of AI and life sciences, exploring the future of intelligent systems through the lens of nature’s laws.

We invite researchers from academia, industry, and interdisciplinary fields to submit their latest contributions and reviews in order to contribute to this promising and rapidly developing field.

Dr. Sebelan Danishvar
Dr. Sobhan Sheykhivand
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 250 words) can be sent to the Editorial Office for assessment.

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

bio-inspired intelligence
biomimetics
neural networks
artificial intelligence
brain-inspired computing
cognitive systems
adaptive algorithms
nature-inspired design
neurodynamics
machine learning
intelligent robotics
computational neuroscience
evolutionary computation
hybrid AI systems
smart bio-systems

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Published Papers (7 papers)

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Research

33 pages, 5767 KB

Open AccessArticle

Hyper-Thyro Vision: An Integrated Framework for Hyperthyroidism Diagnostic Facial Image Analysis Based on Deep Learning

by Poonyisa Thepmangkorn and Suchada Sitjongsataporn

Biomimetics 2026, 11(3), 210; https://doi.org/10.3390/biomimetics11030210 - 15 Mar 2026

Cited by 1 | Viewed by 759

Abstract

This paper presents an integrated multi-modal framework for detecting hyperthyroidism-associated abnormalities, namely exophthalmos and thyroid-related neck swelling, through the joint analysis of frontal facial and neck images using a deep learning-based approach. The objective of this research is to develop an integrated AI framework that improves hyperthyroid-related abnormality detection by simultaneously analyzing facial images of both the eye and neck based on pattern clinical knowledge. The multi-modal framework mimics a biological visual mechanism by using a dual-pathway architecture that concurrently processes foveal-like details of the eyes and neck. It integrates these high-resolution visual embeddings with quantitative morphological measurements to simulate a clinician’s ability to fuse observation with physical assessment. The proposed system employs a multi-faceted decision-making process derived from three distinct data components: two from frontal face analysis and one from neck region analysis. Specifically, eye regions extracted from facial images are preprocessed using the YOLOv11s model. The proposed system leverages a dual-pathway processing architecture to extract comprehensive diagnostic features. For the eye dataset, the framework utilizes a face mesh-based eye landmark (FMEL) to extract both eye regions and perform eyes unfold processing. These regions are subsequently analyzed by the proposed sclera map unwrapping engine (SMUE) to derive quantitative sclera metrics from both the left and right eyes. To optimize classification, a dual-branch architecture is employed by integrating CNN visual embeddings with SMUE-derived statistical features through a feature fusion layer. Simultaneously, the neck processing path executes the neck region of interest (ROI) prediction {upper, lower} to segment critical regions for goiter assessment via the proposed neck

μ - σ

ensemble thresholding (NSET) algorithm. The experimental results demonstrate that the proposed algorithm for eye analysis achieved a mean average precision (mAP50) of 96.4%, with a specific mAP50 of 98.6% for the hyperthyroid class. Regarding quantitative scleral measurement, the SMUE process revealed distinct morphological differences, with the experimental data group exhibiting consistently higher pixel distances across the reference points compared with the normal group. Furthermore, the proposed NSET algorithm yielded the highest performance for swollen neck classification with an mAP50 of 92.0%, significantly outperforming the baseline deep learning models while maintaining lower computational complexity. Full article

(This article belongs to the Special Issue Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation)

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30 pages, 1397 KB

Open AccessArticle

GAN-Based Cross-Modality Brain MRI Synthesis: Paired Versus Unpaired Training and Comparison with Diffusion and Transformer Models

by Behnam Kiani Kalejahi, Sebelan Danishvar and Mohammad Javad Rajabi

Biomimetics 2026, 11(3), 175; https://doi.org/10.3390/biomimetics11030175 - 2 Mar 2026

Viewed by 1196

Abstract

Incomplete or faulty MRI sequences are common in clinical practice and can impair AI-based analyses that rely on complete multi-contrast data. The relative effectiveness of classical generative adversarial networks (GANs) versus modern diffusion and transformer-based models for clinically usable MRI synthesis remains unclear. This study evaluates cross-modality MRI synthesis using the BraTS 2019 brain tumour dataset, focusing on T1-to-T2 translation. We assess paired and unpaired CycleGAN models and compare them with two stronger but computationally intensive baselines, a conditional denoising diffusion probabilistic model (DDPM) and a transformer-enhanced GAN, using identical data splits and preprocessing pipelines. Inter-modality correlation was evaluated to estimate the achievable similarity between modalities. Conceptually, modality synthesis may be viewed as a representation-learning approach that compensates for missing imaging information by reconstructing clinically relevant features from available contrasts. Paired CycleGAN achieved correlations of

r \approx 0.92 - 0.93

and SSIM

\approx 0.90 - 0.92

, approaching natural T1–T2 correlation (

r \approx 0.95

) while maintaining very fast inference (<50 ms/slice). Unpaired CycleGAN achieved

r \approx 0.74 - 0.78

and SSIM

\approx 0.82 - 0.85

, producing clinically interpretable reconstructions without voxel-level supervision. DDPM achieved the highest fidelity (SSIM

\approx 0.93 - 0.95

r \approx 0.94

) but required substantially greater computational resources, while transformer-enhanced GAN performance was intermediate. Qualitative analysis showed that CycleGAN and DDPM best preserved tumour and tissue boundaries, whereas unpaired CycleGAN occasionally over-smoothed subtle lesions. These findings highlight the trade-off between fidelity and efficiency in cross-modality MRI synthesis, suggesting paired CycleGAN for time-sensitive clinical workflows and diffusion models as a computationally expensive accuracy upper bound. Full article

(This article belongs to the Special Issue Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation)

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32 pages, 7073 KB

Open AccessArticle

Crack Contour Modeling Based on a Metaheuristic Algorithm and Micro-Laser Line Projection

by J. Apolinar Muñoz Rodríguez

Biomimetics 2026, 11(2), 102; https://doi.org/10.3390/biomimetics11020102 - 2 Feb 2026

Viewed by 518

Abstract

Currently, bio-inspired metaheuristic algorithms play an important role in computer vision for assessing surface cracks. Also, manufacturing industries need non-destructive technologies based on biomimetics theory for characterizing micro-crack contours to determine surface quality. In this way, it is necessary to develop bio-inspired algorithms to construct crack contour models for determining crack regions through an optical microscope system. In this study, a metaheuristic genetic algorithm is implemented to build crack contour models by means of Bezier functions and crack coordinates. The contour modeling is performed by a microscope vision system based on micro-laser line scanning, which provides the crack coordinates through a broken laser line in the crack region. Thus, the metaheuristic algorithm builds the crack contour model by fitting the Bezier functions toward the crack topography. At this stage, an objective function moves the Bezier functions toward the crack topography via control points. The proposed technique provides micro-scale crack contours with a relative error smaller than 2%. Thus, the proposed crack contour modeling enhances the traditional crack contour inspection based on microscope image processing. This contribution is supported by a comparison between the proposed technique and the crack characterization performed via conventional image processing algorithms. Full article

(This article belongs to the Special Issue Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation)

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24 pages, 2559 KB

Open AccessArticle

A Symmetric Encoder–Decoder Network with Enhanced Group–Shuffle Modules for Robust Lung Nodule Detection in CT Scans

by Mohammad A. Thanoon, Siti Raihanah Abdani, Ahmad Asrul Ibrahim, Asraf Mohamed Moubark, Nor Azwan Mohamed Kamari, Muhammad Ammirrul Atiqi Mohd Zainuri, Mohd Hairi Mohd Zaman and Mohd Asyraf Zulkifley

Biomimetics 2026, 11(2), 92; https://doi.org/10.3390/biomimetics11020092 - 1 Feb 2026

Viewed by 550

Abstract

Lung cancer is considered to be a significant cause of death in the world, and the timely identification of nodules in the lungs in CT scans is very important to enhance the prognosis of patients. Although the state of the art of nodule delineation using deep learning-based segmentation models was achieved, major problems, including high feature diversity, low spatial discrimination, and overfitting of the models, require stronger feature-processing approaches. This research explores an enhanced symmetric encoder–decoder segmentation network known as the Improved Group–Shuffle Module (IGSM) to overcome these shortcomings. The most important feature of the proposed method is the IGSM, which hierarchically divides feature maps into a few groups, then transforms them independently, and then randomly switches channels between groups to increase inter-group interaction of features and diversity. This IGSM method is inspired by human brain functions, which are processed in specialized cortex areas, which are mimicked in this work through small-group feature processing. Channel shuffling is designed based on inter-modular communication in the human brain through coherent information sharing among the small groups of cortices. Through this mechanism, the model is much better at capturing discriminative spatial and contextual patterns, especially on complex and subtle nodule structures. The IGSM configurations have been optimized, specifically, the placement of the modules, grouping size, and shuffle permutation strategies. The proposed model’s performance is then compared with the benchmarked models, like U-Net and DeepLab, with various performance indicators such as mean Intersection over Union (mIoU), Dice Score, Accuracy, Sensitivity, and Specificity. The simulation results proved the superiority of the IGSM-enhanced model with the mIoU of 0.7735, the Dice Score of 0.9665, and the Accuracy of 0.9873. The addition of the group and shuffle module not only enhances the discrimination between the nodules and their background, but it also improves the ability to generalize over a variety of nodules’ morphology, thus producing a reliable tool for automated detection of lung cancer. Full article

(This article belongs to the Special Issue Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation)

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27 pages, 7153 KB

Open AccessArticle

State-Dependent CNN–GRU Reinforcement Framework for Robust EEG-Based Sleep Stage Classification

by Sahar Zakeri, Somayeh Makouei and Sebelan Danishvar

Biomimetics 2026, 11(1), 54; https://doi.org/10.3390/biomimetics11010054 - 8 Jan 2026

Viewed by 1065

Abstract

Recent advances in automated learning techniques have enhanced the analysis of biomedical signals for detecting sleep stages and related health abnormalities. However, many existing models face challenges with imbalanced datasets and the dynamic nature of evolving sleep states. In this study, we present a robust algorithm for classifying sleep states using electroencephalogram (EEG) data collected from 33 healthy participants. We extracted dynamic, brain-inspired features, such as microstates and Lempel–Ziv complexity, which replicate intrinsic neural processing patterns and reflect temporal changes in brain activity during sleep. An optimal feature set was identified based on significant spectral ranges and classification performance. The classifier was developed using a convolutional neural network (CNN) combined with gated recurrent units (GRUs) within a reinforcement learning framework, which models adaptive decision-making processes similar to those in biological neural systems. Our proposed biomimetic framework illustrates that a multivariate feature set provides strong discriminative power for sleep state classification. Benchmark comparisons with established approaches revealed a classification accuracy of 98% using the optimized feature set, with the framework utilizing fewer EEG channels and reducing processing time, underscoring its potential for real-time deployment. These findings indicate that applying biomimetic principles in feature extraction and model design can improve automated sleep monitoring and facilitate the development of novel therapeutic and diagnostic tools for sleep-related disorders. Full article

(This article belongs to the Special Issue Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation)

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31 pages, 5285 KB

Open AccessArticle

Ensemble Deep Learning for Real–Bogus Classification with Sky Survey Images

by Pakpoom Prommool, Sirikan Chucherd, Natthakan Iam-On and Tossapon Boongoen

Biomimetics 2025, 10(11), 781; https://doi.org/10.3390/biomimetics10110781 - 17 Nov 2025

Viewed by 1054

Abstract

The discovery of the fifth gravitational wave, GW170817, and its electromagnetic counterpart, resulting from the merger of neutron stars by the LIGO and Virgo teams, marked a major milestone in astronomy. It was the first time that gravitational waves and light from the same cosmic event were observed simultaneously. The LIGO detectors in the United States recorded the signal for 100 s, longer than in previous detections. The merging of neutron stars emits both gravitational and electromagnetic waves across all frequencies—from radio to gamma rays. However, pinpointing the exact source remains difficult, requiring rapid sky scanning to locate it. To address this challenge, the Gravitational-Wave Optical Transient Observer (GOTO) project was established. It is specifically designed to detect optical light from transient events associated with gravitational waves, enabling faster follow-up observations and a deeper study of these short-lived astronomical phenomena, which appear and disappear quickly in the universe. In astrophysics, it has become more important to find astronomical transient events like supernovae, gamma-ray bursts, and stellar flares because they are linked to extreme cosmic processes. However, finding these short-lived events in huge sky survey datasets, like those from the GOTO project, is very hard for traditional analysis methods. This study suggests a deep learning methodology employing Convolutional Neural Networks (CNNs) to enhance transient classification. CNNs are based on how biological vision systems work and how they are structured. They mimic how animal brains hierarchically process visual information, making it possible to automatically find complex spatial patterns in astronomical images. Transfer learning and fine-tuning on pretrained ImageNet models are utilized to emulate adaptive learning observed in biological organisms, enabling swift adaptation to new tasks with minimal data. Data augmentation methods like rotation, flipping, and noise injection mimic changes in the environment to improve model generalization. Dropout and different batch sizes are used to stop overfitting, which is similar to how biological systems use redundancy and noise tolerance. Ensemble learning strategies, such as Soft Voting and Weighted Voting, draw inspiration from collective intelligence in biological systems, integrating multiple CNN models to enhance decision-making robustness. Our findings indicate that this bio-inspired framework substantially improves the precision and dependability of transient detection, providing a scalable solution for real-time applications in extensive sky surveys such as GOTO. Full article

(This article belongs to the Special Issue Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation)

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25 pages, 1071 KB

Open AccessArticle

New Binary Reptile Search Algorithms for Binary Optimization Problems

by Broderick Crawford, Benjamín López Cortés, Felipe Cisternas-Caneo, José Manuel Gómez-Pulido, Rodrigo Olivares, Ricardo Soto, José Barrera-Garcia, Cristóbal Brante-Aguilera and Giovanni Giachetti

Biomimetics 2025, 10(10), 653; https://doi.org/10.3390/biomimetics10100653 - 1 Oct 2025

Viewed by 1038

Abstract

Binarizing continuous metaheuristics to solve challenging NP-hard binary optimization problems is a fundamental step in adapting continuous algorithms for discrete domains. Binary optimization problems, such as the Set Covering Problem and the 0–1 Knapsack Problem, demand tailored approaches to efficiently explore and exploit the solution space. The process of binarization often introduces complexities, as it requires balancing the transformation of continuous populations into binary solutions while preserving the algorithm’s capability to navigate the search space effectively. In this context, we explore the performance of the Reptile Search Algorithm (RSA), a continuous metaheuristic, applied to these two benchmark problems. To address the binary nature of the problems, a two-step binarization process is implemented, utilizing combinations of transfer functions with binarization rules. This framework enables the RSA to generate binary solutions while leveraging its inherent strengths in exploration and exploitation. Comparative experiments are conducted with Particle Swarm Optimization and the Grey Wolf Optimizer to benchmark the RSA’s performance under similar conditions. These experiments analyze critical factors such as fitness values, convergence behavior, and exploration–exploitation dynamics, providing insights into the effectiveness of different binarization approaches. The results demonstrate that the RSA achieves competitive performance across both problems, highlighting its flexibility and adaptability, which are attributed to its diverse movement equations. Notably, the Z4 transfer function consistently enhances performance for all algorithms, even when paired with less effective binarization rules. This indicates the potential of Z4 as a robust transfer function for binary optimization. The findings underscore the importance of selecting appropriate binarization strategies to maximize the performance of continuous metaheuristics in binary domains, paving the way for further advancements in hybrid optimization methodologies. Full article

(This article belongs to the Special Issue Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation)

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