Foundation models aren't dominating NeuroAI yet, but they're gaining traction as tools for exploring brain-inspired mechanisms, cognitive representations, and emergent properties.
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With how broadly/loosely defined NeuroAI is, I'd be pretty cautious about predicting that any one method will dominate the entire field. Maybe I'm being naive, but I find it hard to imagine that methods involving simpler statistical models would invariably be improved by using a huge neural network.
yea I also have some faith that people will use the tools that are most fit for the job, and any single common foundation model is unlikely to be useful to everyone.
Yup! Not to mention the significant investment in computing power, time, and energy that would be required to run and implement (much less fine-tune or train from scratch) these sorts of models, especially for a neuroscience lab that may not have had this type of infra to begin with.
And don't get me wrong, I think foundation models for neuroscience are yielding fascinating results (currently doing some related work myself!) -- I just have less faith that they'll dominate NeuroAI to the extent that I think they will fields like language, vision, robotics, etc.
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