Understanding the dynamic organization of neural networks is crucial for elucidating fundamental principles governing brain function. In this study, we focused on identifying and characterizing subnetworks composed of three dynamically interacting neurons in the hippocampus of mice. Employing calcium imaging techniques, neural activity patterns were recorded as mice navigated in a circular track. The developed network reconstruction algorithm is based on the coincidence of calcium spikes. Using the obtained networks, a new approach was introduced to detect triples of cellular activity and evaluate their associations with mouse behavioral states. Statistical significance was confirmed by comparison with randomized datasets generated. Associations of these triples in the experimental data with different mouse activities were found. The implementations of computationally optimized algorithms are published online. Our findings emphasize the necessity of accounting for higher-order interactions in neural circuitry, offering new insights into the mechanisms underlying cognition and memory formation.