Gayathri Ramesan (she/her)

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What neuroscience can tell AI about learning in continuously changing environments Nature Machine Intelligence - Durstewitz et al. explore what artificial intelligence can learn from the brain’s ability to adjust quickly to changing environments. By linking neuroscience...

Unlike current AI systems, animals can quickly and flexibly adapt to changing environments.

This is the topic of our new perspective in Nature MI (rdcu.be/eSeif), where we relate dynamical and plasticity mechanisms in the brain to in-context and continual learning in AI. #NeuroAI

Congratulations @koch-lab.bsky.social !

What Neuroscience Can Teach AI About Learning in Continuously Changing Environments Modern AI models, such as large language models, are usually trained once on a huge corpus of data, potentially fine-tuned for a specific task, and then deployed with fixed parameters. Their training ...

We wrote a little #NeuroAI piece about in-context learning & neural dynamics vs. continual learning & plasticity, both mechanisms to flexibly adapt to changing environments:
arxiv.org/abs/2507.02103
We relate this to non-stationary rule learning tasks with rapid performance jumps.

Feedback welcome!

Homeorhetic regulation of cellular phenotype How cells translate growth factor (GF) signals into context-specific phenotypes remains a fundamental question in cell biology. The classical view holds that cells translate a constant concentration o...

👇🏻👇🏻👇🏻 Pre-print alert 👇🏻👇🏻👇🏻
We developed the concept of signaling homeorhesis to describe regulation of cell phenotype under dynamic signals, demonstrating that cells actively interpret signals from their environment to determine cell fate! @mpinb.mpg.de

www.biorxiv.org/content/10.1...

Happy to welcome Kalel Rossi, a new postdoc in the group, Prayas Chakrabarty, Master student, and Abbie Chen, student assistant! #CellularComputationsAndLearning @mpinb.mpg.de