Bio Inspired
Bio-inspired research draws inspiration from biological systems to solve engineering and computational challenges, aiming to create more efficient, robust, and adaptable artificial systems. Current research focuses on applying bio-inspired principles to robotics (e.g., locomotion, navigation, exploration), computer vision (e.g., memory, object recognition, scene reconstruction), and machine learning (e.g., continual learning, optimization algorithms), often employing neural networks, spiking neural networks, and evolutionary computation. This interdisciplinary field is significant for advancing both fundamental understanding of biological systems and the development of novel technologies with improved performance and capabilities in various applications.
Papers
Bio-inspired spike-based Hippocampus and Posterior Parietal Cortex models for robot navigation and environment pseudo-mapping
Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, Juan P. Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno, Fernando Perez-Pena
A bioinspired three-stage model for camouflaged object detection
Tianyou Chen, Jin Xiao, Xiaoguang Hu, Guofeng Zhang, Shaojie Wang