This study presents a potentially groundbreaking training strategy for neural networks, with the objective of achieving authentic multimodal functionality and improved generalizability. Inspired by the remarkable versatility of the human brain and the multifunctionality exhibited by human organs, such as the tongue’s capacity to perceive temperature, taste, texture, and humidity, we propose a novel training approach and architecture. Diverging from traditional task-specific models and existing multimodal models—wherein multimodality is achieved by connecting multiple models or utilizing deep autoencoders that primarily consider cross-modality learning during feature extraction while utilizing a single modality for supervised training and testing.