Computational Models of Multisensory Integration with Recurrent Neural Networks: A Critical Review and Future Directions

Abstract

Multisensory integration (MSI) is a core brain function underlying perception, learning, and behavior. Understanding the computational mechanisms of MSI is key to advancing AI and brain-inspired systems. While earlier models relied on probabilistic frameworks, recurrent neural networks (RNNs) offer advantages in capturing temporal dynamics and neural computations. This review presents a critical examination of computational models of MSI, focusing on the evolution from probabilistic integration to modern RNN-based methods. Biological evidence for temporal coordination in multisensory areas is analyzed and explored how different RNN architectures (e.g., vanilla, long short-term memory, and gated recurrent unit) simulate these dynamics. Comparative analyses show RNNs’ superiority in robustness and learning efficiency, with up to 46.9% improvement in classification tasks involving sensory fusion. We introduce a taxonomy of MSI tasks and a novel evaluation framework for model benchmarking. Real-world case studies—from speech recognition to prosthetic control—highlight practical applications. Challenges in interpretability, data efficiency, and generalization are also discussed. The review provides actionable insights for future research in both computational neuroscience and artificial intelligence. By bridging neurobiological principles and machine learning, RNN-based models pave the way for intelligent systems capable of flexible, context-aware multisensory processing. © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.