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Human perception plays a vital role in forming beliefs and understandingreality. A deeper understanding of brain functionality will lead to thedevelopment of novel deep neural networks. In this work, we introduce a novelframework named Brainformer, a straightforward yet effective Transformer-basedframework, to analyze Functional Magnetic Resonance Imaging (fMRI) patterns inthe human perception system from a machine-learning perspective. Specifically,we present the Multi-scale fMRI Transformer to explore brain activity patternsthrough fMRI signals. This architecture includes a simple yet efficient modulefor high-dimensional fMRI signal encoding and incorporates a novel embeddingtechnique called 3D Voxels Embedding. Secondly, drawing inspiration from thefunctionality of the brain's Region of Interest, we introduce a novel lossfunction called Brain fMRI Guidance Loss. This loss function mimics brainactivity patterns from these regions in the deep neural network using fMRIdata. This work introduces a prospective approach to transferring knowledgefrom human perception to neural networks. Our experiments demonstrate thatleveraging fMRI information allows the machine vision model to achieve resultscomparable to State-of-the-Art methods in various image recognition tasks.

Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI