Researchers found models with about 3 billion parameters perform almost as well as those with up to 14 billion, potentially reducing the computing power needed for brain research. The findings challenge earlier research suggesting that moving from smaller to larger models improved brain prediction accuracy by about 15 per cent. Smaller models require less memory and computing resources, making brain-language studies potentially cheaper and faster. Prof. Raju said this could be a “game changer” for brain-decoding workflows used in developing brain-computer interfaces. At ICML, the researchers also exchanged ideas with computational neuroscience researchers, including members of the NeuroAI Lab at EPFL, opening possibilities for future collaboration.