黑料吃瓜资源

What does it take to make AI that can pass as human? Try massive clusters of supercomputers. To build human-like intelligence, computer scientists think big. However, for neuroscientists who want to understand how real brains work, today鈥檚 AI only goes so far, as it replaces one deeply complicated system (the brain) with another (AI). How then do we figure out the inner workings of the biological brain? To answer this question, 黑料吃瓜资源 Assistant Professor Benjamin Cowley is thinking small.

In collaboration with Carnegie Mellon University Professor and Princeton University Professor , Cowley has helped develop a new AI model much smaller and simpler than today鈥檚 鈥渟tate-of-the-art鈥� systems, yet far better at illustrating how the brain makes sense of visual stimuli. In previous work, Cowley trained AI to anticipate neural responses in fruit flies. This time, he鈥檚 set his sights on macaque, a species of monkey whose brains are much closer to humans.

In a new study published in Nature, Cowley and colleagues present macaques with sets of carefully curated natural images and track which neurons in the animals鈥� visual cortex fire in response to each picture. From there, they first train large AI models to predict neural responses to specific images until they outperform competing models by more than 30%. Then, they use compression technology to shrink the large AI model to about 1/1,000 the size. The result is a vision model small enough for an email attachment.

Finding that AI models of the brain could be this tiny is huge in itself. But Cowley goes further, pinpointing the inner workings of these models. This analysis reveals something extraordinary. The compact model neurons all break down images into low-level features like edges and colors, then form unique preferences by consolidating this information in different ways. What does this mean for primates like us? Cowley offers one example.

鈥淚n the monkey鈥檚 brain鈥攁nd in our brains, too, most likely鈥攖here鈥檚 a group of V4 neurons that love dots.鈥�

In other words, there are neurons in your brain that specialize in dot detection. That might seem random, but think about the key features of the face. What are eyes but dots loaded with information? Consider how important eye contact is in daily life.

Looking ahead, the findings have Cowley thinking about building AI models of mental health conditions. 鈥淔or example, in Alzheimer鈥檚 dementia, we know synapses are lost,鈥� he explains. 鈥淚f we know the images that drive neurons to talk to each other, we can potentially rebuild synapses once thought lost to disease.鈥�

Who knows? Thanks to work like this, one day you might be able to stave off鈥攐r even treat鈥攏eurodegenerative disease by looking at special pictures. Just wait and see.

Written by: Samuel Diamond, Senior Communications Strategist | [email protected] | 516-367-5055

Funding

The C.V. Starr Foundation, National Eye Institute, Simons Collaboration on the Global Brain, National Institutes of Health BRAIN Initiative, National Institute of Mental Health, National Science Foundation

Citation

Cowley, B., Stan, P., Pillow, J., Smith, M, 鈥淐ompact deep neural network models of the visual cortex鈥�, Nature, February 25, 2026. DOI: