@article{126,
  keywords = {BigData, Brain-machine interface, Embedding, Foundation model, generative AI, Representation learning, Self-supervised learning, Transfer learning, Transformer},
  author = {Ran Wang and Zhe Chen},
  title = {Large-scale foundation models and generative AI for BigData neuroscience.},
  abstract = {<p>Recent advances in machine learning have led to 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.</p>},
  year = {2025},
  journal = {Neuroscience research},
  volume = {215},
  pages = {3-14},
  month = {06/2025},
  issn = {1872-8111},
  language = {eng},
}