Large-scale Foundation Models and Generative AI for BigData Neuroscience
This is a mini-review that discusses how these models could impact neuroscience research, but it is incremental as it synthesizes existing knowledge without presenting new findings.
The paper reviews recent advances in foundation models and generative AI, exploring their potential applications in neuroscience areas such as natural language processing, brain-machine interfaces, and data augmentation, but does not report specific experimental results or concrete numbers.
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.