Tracking multiple objects (MOT) is an important topic in almost all computer vision-related areas. One of the most vital challenges in front of MOT’s algorithms is data association, particularly when partial or complete occlusions occur. On the other hand, the human can handle this challenge in everyday scenarios for example while driving a car on a highway or riding a bicycle. Accordingly, we used a brain-inspired method to propose an MOT algorithm that can overcome the above challenge. The proposed method is based on dynamic neural field as a brain-inspired algorithm that can mimic both neural and cognitive functions of the brain. Besides, we benefited from computer vision techniques to find targets and extract features such as their locations, directions, and velocities. We applied our method on an online dataset containing videos recorded from natural movements of zebrafish larvae. Evaluation results using two metrics MOTA and MOTP showed that the proposed method has a promising performance compared to the state-of-the-art algorithms. It can associate all information correctly both in the presence and absence of occlusion events.