Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence

TL;DR

This paper introduces an inverse effectiveness driven multimodal fusion (IEMF) strategy inspired by brain mechanisms, improving multimodal AI performance and efficiency across various tasks and neural network types.

Contribution

It proposes a novel biologically inspired fusion method that enhances integration efficiency and reduces computational costs in multimodal neural networks.

Findings

Achieves up to 50% reduction in computational cost.

Demonstrates improved performance on audio-visual classification, continual learning, and question answering.

Shows good adaptability to both ANN and SNN architectures.

Abstract

Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion research typically assumes static integration, not fully incorporating key dynamic mechanisms found in the brain. Specifically, the brain exhibits an inverse effectiveness phenomenon, wherein weaker unimodal cues yield stronger multisensory integration benefits; conversely, when individual modal cues are stronger, the effect of fusion is diminished. This mechanism enables biological systems to achieve robust cognition even with scarce or noisy perceptual cues. Inspired by this biological mechanism, we explore the relationship between multimodal output and information from individual modalities, proposing an inverse effectiveness driven multimodal fusion (IEMF) strategy. By incorporating this strategy into neural networks, we achieve more efficient integration with improved model performance and computational efficiency, demonstrating up to 50% reduction in computational cost across diverse fusion methods. We conduct experiments on audio-visual classification, continual learning, and question answering tasks to validate our method. Results consistently demonstrate that our method performs excellently in these tasks. To verify universality and generalization, we also conduct experiments on Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN), with results showing good adaptability to both network types. Our research emphasizes the potential of incorporating biologically inspired mechanisms into multimodal networks and provides promising directions for the future development of multimodal artificial intelligence. The code is available at https://github.com/Brain-Cog-Lab/IEMF.