Brain-Inspired AI Learns To See Like Humans in Stunning Vision Breakthrough

By Institute for Basic Science May 22, 2025

Collected at: https://scitechdaily.com/brain-inspired-ai-learns-to-see-like-humans-in-stunning-vision-breakthrough/

The IBS-Yonsei research team introduces a novel Lp-Convolution method at ICLR 2025.

A team of researchers from the Institute for Basic Science (IBS), Yonsei University, and the Max Planck Institute has developed a new artificial intelligence (AI) technique that brings machine vision closer to the way the human brain processes visual information. Known as Lp-Convolution, this method enhances the accuracy and efficiency of image recognition systems while also lowering the computational demands of traditional AI models.

Bridging the Gap Between CNNs and the Human Brain

The human brain excels at quickly identifying important features within complex visual scenes, a level of efficiency that conventional AI systems have struggled to achieve. Convolutional Neural Networks (CNNs), the most commonly used models for image recognition, analyze images using small, fixed square-shaped filters. While effective to a degree, this design limits their ability to detect wider patterns in fragmented or variable data.

Vision Transformers (ViTs) have more recently outperformed CNNs by evaluating entire images simultaneously. However, their success comes at a cost, they require enormous computing power and vast datasets, making them less feasible for practical, large-scale deployment.

Information Processing Structures of the Brain’s Visual Cortex and Artificial Neural Networks — 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. Credit: Institute for Basic Science

Inspired by how the brain’s visual cortex processes information selectively through circular, sparse connections, the research team sought a middle ground: Could a brain-like approach make CNNs both efficient and powerful?

Introducing LP-Convolution: A Smarter Way to See

To answer this, the team developed Lp-Convolution, a novel method that uses a multivariate p-generalized normal distribution (MPND) to reshape CNN filters dynamically. Unlike traditional CNNs, which use fixed square filters, Lp-Convolution allows AI models to adapt their filter shapes, stretching horizontally or vertically based on the task, much like how the human brain selectively focuses on relevant details.

This breakthrough solves a long-standing challenge in AI research, known as the large kernel problem. Simply increasing filter sizes in CNNs (e.g., using 7×7 or larger kernels) usually does not improve performance, despite adding more parameters. Lp-Convolution overcomes this limitation by introducing flexible, biologically inspired connectivity patterns.

Real-World Performance: Stronger, Smarter, and More Robust AI

In tests on standard image classification datasets (CIFAR-100, TinyImageNet), Lp-Convolution significantly improved accuracy on both classic models like AlexNet and modern architectures like RepLKNet. The method also proved to be highly robust against corrupted data, a major challenge in real-world AI applications.

Moreover, the researchers found that when the Lp-masks used in their method resembled a Gaussian distribution, the AI’s internal processing patterns closely matched biological neural activity, as confirmed through comparisons with mouse brain data.

Brain Inspired Design of LP Convolution — The brain processes visual information using a Gaussian-shaped connectivity structure that gradually spreads from the center outward, flexibly integrating a wide range of information. In contrast, traditional CNNs face issues where expanding the filter size dilutes information or reduces accuracy (d, e). To overcome these structural limitations, the research team developed Lp-Convolution, inspired by the brain’s connectivity (a–c). This design spatially distributes weights to preserve key information even over large receptive fields, effectively addressing the shortcomings of conventional CNNs. Credit: Institute for Basic Science

“We humans quickly spot what matters in a crowded scene,” said Dr. C. Justin LEE, Director of the Center for Cognition and Sociality within the Institute for Basic Science. “Our LP-Convolution mimics this ability, allowing AI to flexibly focus on the most relevant parts of an image, just like the brain does.”

Impact and Future Applications

Unlike previous efforts that either relied on small, rigid filters or required resource-heavy transformers, Lp-Convolution offers a practical, efficient alternative. This innovation could revolutionize fields such as:

Autonomous driving, where AI must quickly detect obstacles in real time
Medical imaging, improving AI-based diagnoses by highlighting subtle details
Robotics, enabling smarter and more adaptable machine vision under changing conditions

“This work is a powerful contribution to both AI and neuroscience,” said Director C. Justin Lee. “By aligning AI more closely with the brain, we’ve unlocked new potential for CNNs, making them smarter, more adaptable, and more biologically realistic.”

Looking ahead, the team plans to refine this technology further, exploring its applications in complex reasoning tasks such as puzzle-solving (e.g., Sudoku) and real-time image processing.

Reference: “Brain-inspired Lp-Convolution benefits large kernels and aligns better with visual cortex” by Jea Kwon, Sungjun Lim, Kyungwoo Song and C. Justin Lee, 11 March 2025, ICLR 2025.

The study will be presented at the International Conference on Learning Representations (ICLR) 2025, and the research team has made their code and models publicly available: https://github.com/jeakwon/lpconv/

Funding: Institute for Basic Science