Large-scale Foundation Models and Generative AI for BigData Neuroscience
Ran Wang Zhe Sage Chen
Recent advances in machine learning have made revolutionary breakthroughs in
computer games, image and natural language understanding, and scientific
discovery. Foundation models and large-scale language models (LLMs) have
recently achieved human-like intelligence thanks to BigData. With the help of
self-supervised learning (SSL) and transfer learning, these models may
potentially reshape the landscapes of neuroscience research and make a
significant impact on the future. Here we present a mini-review on recent
advances in foundation models and generative AI models as well as their
applications in neuroscience, including natural language and speech, semantic
memory, brain-machine interfaces (BMIs), and data augmentation. We argue that
this paradigm-shift framework will open new avenues for many neuroscience
research directions and discuss the accompanying challenges and opportunities.