Understanding how the brain processes what we see is one of the central questions in neuroscience. Our visual system is incredibly powerful, able to recognize faces, objects, and scenes with ease, yet the details of how individual neurons respond to images remain complex and difficult to study. A new study published in Nature shows that it is possible to capture these responses using models that are both highly accurate and far simpler than previous approaches.
The team began with a large computer model designed to predict how neurons in the visual cortex of non-human subjects respond to images. While this model was very precise, it was also enormous, with millions of parameters, making it almost as hard to understand as the brain itself. Using advanced machine learning techniques, the researchers compressed this model, creating smaller versions that were thousands of times simpler while still predicting neural responses with high accuracy. These compact models allowed the team to examine the inner workings of the visual system in a way that was previously impossible.
"This work shows that we don't need massive, complicated networks to understand what individual neurons are doing," explained Matt Smith, professor of biomedical engineering and Neuroscience Institute at Carnegie Mellon University. "By making the models smaller and interpretable, we can actually gain intuition about how the visual system works and develop hypotheses that can be tested in the lab."
One surprising finding was that even though the model was dramatically reduced in size, it could still capture subtle differences in how neurons respond to similar images. This suggests that the brain's visual system relies on specific computational patterns that can be represented in a more straightforward way than previously thought. By studying these simplified models, researchers could see how individual neurons detect important features, such as the eyes in a face or the dots in a pattern, offering insights into how visual information is processed at a fine scale.
Beyond helping scientists understand vision, this research also has implications for technology. Modern computer vision systems, like those that recognize faces on a phone or guide self-driving cars, were inspired by the brain but often fail in subtle ways that humans easily handle. Insights from these compact neural models could help improve artificial intelligence systems, making them more robust and adaptable in real-world situations.
"The study also highlights the collaborative nature of this research, combining experimental neuroscience with computational modeling and machine learning," Smith added. "By working together across institutions and disciplines, we were able to build models that are not only predictive, but also interpretable and meaningful."
Looking ahead, the researchers are extending these models to account for time, moving from single images to sequences like videos. This could help explain how the visual system tracks movement, recognizes changing patterns, and focuses on important details in dynamic environments. By continuing to simplify and study these models, the collaborators hope to uncover rules that govern how our brains interpret the world around us.
