Linyang He
@linyanghe.bsky.social
PhD Student @ Mesgarani Lab, Columbia University
Neuroscience+ML+Language
https://linyanghe.github.io/
Neuroscience+ML+Language
https://linyanghe.github.io/
Pinned
🧠 New at #NeurIPS2025!
🎵 We're far from the shallow now🎵
TL;DR: We introduce the first "reasoning embedding" and uncover its unique spatio-temporal pattern in the brain.
🔗 arxiv.org/abs/2510.228...
🎵 We're far from the shallow now🎵
TL;DR: We introduce the first "reasoning embedding" and uncover its unique spatio-temporal pattern in the brain.
🔗 arxiv.org/abs/2510.228...
🧠 New at #NeurIPS2025!
🎵 We're far from the shallow now🎵
TL;DR: We introduce the first "reasoning embedding" and uncover its unique spatio-temporal pattern in the brain.
🔗 arxiv.org/abs/2510.228...
🎵 We're far from the shallow now🎵
TL;DR: We introduce the first "reasoning embedding" and uncover its unique spatio-temporal pattern in the brain.
🔗 arxiv.org/abs/2510.228...
October 30, 2025 at 10:25 PM
🧠 New at #NeurIPS2025!
🎵 We're far from the shallow now🎵
TL;DR: We introduce the first "reasoning embedding" and uncover its unique spatio-temporal pattern in the brain.
🔗 arxiv.org/abs/2510.228...
🎵 We're far from the shallow now🎵
TL;DR: We introduce the first "reasoning embedding" and uncover its unique spatio-temporal pattern in the brain.
🔗 arxiv.org/abs/2510.228...
Reposted by Linyang He
🌍Introducing BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data!
LLMs learn from vastly more data than humans ever experience. BabyLM challenges this paradigm by focusing on developmentally plausible data
We extend this effort to 45 new languages!
LLMs learn from vastly more data than humans ever experience. BabyLM challenges this paradigm by focusing on developmentally plausible data
We extend this effort to 45 new languages!
October 15, 2025 at 10:53 AM
🌍Introducing BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data!
LLMs learn from vastly more data than humans ever experience. BabyLM challenges this paradigm by focusing on developmentally plausible data
We extend this effort to 45 new languages!
LLMs learn from vastly more data than humans ever experience. BabyLM challenges this paradigm by focusing on developmentally plausible data
We extend this effort to 45 new languages!
Reposted by Linyang He
What happens when you listen to speech a different speeds? Does your brain change its processing speed too? It turns out, no
@samnorman-haignere.bsky.social & researchers at
@zuckermanbrain.bsky.social found the auditory part of the brain keeps clocking in at a fixed time
via @natneuro.nature.com
@samnorman-haignere.bsky.social & researchers at
@zuckermanbrain.bsky.social found the auditory part of the brain keeps clocking in at a fixed time
via @natneuro.nature.com
Temporal integration in human auditory cortex is predominantly yoked to absolute time - Nature Neuroscience
Temporal integration throughout the human auditory cortex is predominantly locked to absolute time and does not vary with the duration of speech structures such as phonemes or words.
www.nature.com
September 18, 2025 at 6:15 PM
What happens when you listen to speech a different speeds? Does your brain change its processing speed too? It turns out, no
@samnorman-haignere.bsky.social & researchers at
@zuckermanbrain.bsky.social found the auditory part of the brain keeps clocking in at a fixed time
via @natneuro.nature.com
@samnorman-haignere.bsky.social & researchers at
@zuckermanbrain.bsky.social found the auditory part of the brain keeps clocking in at a fixed time
via @natneuro.nature.com
Reposted by Linyang He
In our new paper, we explore how we can build encoding models that are both powerful and understandable. Our model uses an LLM to answer 35 questions about a sentence's content. The answers linearly contribute to our prediction of how the brain will respond to that sentence. 1/6
August 18, 2025 at 9:42 AM
In our new paper, we explore how we can build encoding models that are both powerful and understandable. Our model uses an LLM to answer 35 questions about a sentence's content. The answers linearly contribute to our prediction of how the brain will respond to that sentence. 1/6