Visual Information Decoding Based on State-Space Model with Neural Pathways Incorporation

Abstract

In contemporary visual decoding models, traditional neural network-based methods have made some advancements; however, their performance in addressing complex visual tasks remains constrained. This limitation is primarily due to the restrictions of local receptive fields and their inability to effectively capture visual information, resulting in the loss of essential contextual details. Visual processing in the brain initiates in the retina, where information is transmitted via the optic nerve to the lateral geniculate nucleus (LGN) and subsequently progresses along the ventral pathway for layered processing. Unfortunately, this natural process is not fully represented in current decoding models. In this paper, we propose a state-space-based visual information decoding model, SSM-VIDM, which enhances performance in complex visual tasks by aligning with the brain’s visual processing mechanisms. This approach overcomes the limitations of traditional convolutional neural networks (CNNs) regarding local receptive fields, thereby preserving contextual information in visual tasks. Experimental results demonstrate that the state-space-based visual information decoding model proposed in this study outperforms traditional decoding models in terms of performance and exhibits higher accuracy in image recognition tasks. Our research findings suggest that the visual decoding model, which is based on the lateral geniculate nucleus and the ventral pathway, can enhance decoding performance.