BrAMA: A Data-Efficient Brain-Inspired Architecture for Semi-Supervised Multi-Modal Association

Despite their remarkable capabilities, state-of-the-art AI models rely on deeply parameterized architectures that require extensive labeled datasets and multiple training epochs, revealing significant inefficiencies compared to biological intelligence in terms of data utilization. While self-supervised pre-training techniques have advanced the field, these approaches still demand considerable amount of data to achieve high classification accuracy. Biological neural systems, in contrast, demonstrate remarkable efficiency through local learning rules and multi-modal integration. Drawing inspiration from these principles, we present BrAMA (Brain-inspired Architecture for Multimodal Association), a novel framework that constructs meaningful data representations by associating symbolic representation of multimodal signals, drawing inspiration from cognitivist principles. Our key contributions include novel Hebbian local learning rules and architectural improvements to existing brain-inspired approaches. Through parameterized Hebbian connections between self-organizing maps, our enhanced learning mechanisms and its semi-supervision learning capabilities, BrAMA achieves superior accuracy compared to state-of-the-art approaches while requiring significantly fewer training examples with a single epoch.