There is a growing interest in developing foundation models for neural data, i.e. models that are pretrained on large-scale, diverse datasets and which can then be fine-tuned for a variety of downstream tasks with relatively little data. This approach has been extremely powerful in AI research, and will likely help unlock many capabilities for neuro-technology, particularly for brain-machine interface applications, medical diagnosis and treatments, and the development of digital twins as neuroscience discovery platforms. Our collaboration has grown directly from the Frontier Models for Neuroscience & Behavior working group, which has been quite successful in bringing our groups together and also sharing ideas and new work from other groups beyond ARNI.
