Large-scale Foundation Models and Generative AI for BigData Neuroscience

• URL: http://arxiv.org/abs/2310.18377v1
• Date: Fri, 27 Oct 2023 00:44:40 GMT
• Title: Large-scale Foundation Models and Generative AI for BigData Neuroscience
• Authors: Ran Wang and Zhe Sage Chen
• Abstract summary: Recent advances in machine learning have made revolutionary breakthroughs in computer games, image and natural language understanding. 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.
• Score: 3.4825443450916196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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