New Computational Model Enhances Understanding of Neuronal Information Encoding

🧠Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like telling a leaf apart from a rock. But they have struggled to build computational models that are simple enough to allow them to understand what individual neurons are doing. 🥼To address this challenge, researchers in the Stringer and Pachitariu labs at HHMI's Janelia Research Campus set out to create a simpler model to explain what’s going on in the primary visual cortex – the first stop in the brain for visual data. 🍂To do this, they first recorded neural activity in more than 29,000 neurons in the primary visual cortex of a mouse as it viewed up to 65,000 images of natural textures, like leaves and rocks. This video shows the natural images at the top and the corresponding brain imaging data at the bottom. 💻The researchers then tested different models to find the simplest one that could reproduce the visual information. Ultimately, they developed a new class of simplified “minimodels” that can predict the visual responses of individual neurons in the primary visual cortex. 👀Determining what happens in individual neurons in the visual cortex is an important first step in understanding visual processing and could help researchers figure out how other parts of the brain are carrying out more complicated computations.