Large-scale Foundation Models and Generative AI for BigData Neuroscience

TL;DR

This paper reviews recent developments in foundation and generative AI models, highlighting their potential to transform neuroscience research through applications like language, memory, BMIs, and data augmentation.

Contribution

It provides a comprehensive overview of how large-scale models are being applied in neuroscience, emphasizing the paradigm shift enabled by these AI advances.

Findings

Foundation models enhance natural language and speech analysis in neuroscience.

Generative AI models facilitate data augmentation and brain-machine interfaces.

The paradigm shift opens new research avenues and presents unique challenges.

Abstract

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.