Figure 1. Information Processing Structures of the Brain’s Visual Cortex and Artificial Neural Networks (IMAGE)
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In the actual brain’s visual cortex, neurons are connected broadly and smoothly around a central point, with connection strength varying gradually with distance (a, b). This spatial connectivity follows a bell-shaped curve known as a ‘Gaussian distribution,’ enabling the brain to integrate visual information not only from the center but also from the surrounding areas.
In contrast, traditional Convolutional Neural Networks (CNNs) process information by having neurons focus on a fixed rectangular region (e.g., 3×3, 5×5, etc.) (c, d). CNN filters move across an image at regular intervals, extracting information in a uniform manner, which limits their ability to capture relationships between distant visual elements or respond selectively based on importance.
This study addresses the differences between these biological structures and CNNs, proposing a new filter structure called 'Lp-Convolution' that mimics the brain’s connectivity patterns. In this structure, the range and sensitivity of a neuron’s input are designed to naturally spread in a Gaussian-like form, allowing the system to self-adjust during training—emphasizing important information more strongly while downplaying less relevant details. This enables image processing that is more flexible and biologically aligned compared to traditional CNNs.
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