@article{wang_large-scale_2024,
	title = {Large-scale foundation models and generative {AI} for {BigData} neuroscience},
	issn = {0168-0102},
	url = {https://www.sciencedirect.com/science/article/pii/S0168010224000750},
	doi = {10.1016/j.neures.2024.06.003},
	abstract = {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.},
	urldate = {2025-01-06},
	journal = {Neuroscience Research},
	author = {Wang, Ran and Chen, Zhe Sage},
	month = jun,
	year = {2024},
	keywords = {BigData, Brain-machine interface, Embedding, Foundation model, Generative AI, Representation learning, Self-supervised learning, Transfer learning, Transformer},
}

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