Yipeng Li
@moonliyp.bsky.social
75 followers 87 following 17 posts
I do vision neuroscience research, focusing on IT cortex.
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Reposted by Yipeng Li
jbarbosa.org
Let's recap:

Everything* is everywhere**

* Except language, motor, memory, facial recognition, spatial perception, ...

** Except white matter, human brains, or anything else that is not a mouse brain
benhayden.bsky.social
How do you deal with the confound of white matter damage when interpreting natural experiments?
Reposted by Yipeng Li
intlbrainlab.bsky.social
Two flagship papers from the International Brain Laboratory, now out in ‪@Nature.com‬:
🧠 Brain-wide map of neural activity during complex behaviour: doi.org/10.1038/s41586-025-09235-0
🧠 Brain-wide representations of prior information in mouse decision-making: doi.org/10.1038/s41586-025-09226-1 +
moonliyp.bsky.social
A happy recording day, listening to spiking activities from two visual areas with distinct preferences (body and face).
Reposted by Yipeng Li
agreco.bsky.social
🔵 Proud to share our new preprint 🔵

We compared humans and deep neural networks on sound localization 👂📍

Humans robustly localized OOD sounds even without primary interaural cues (ITD & ILD)

Models localized well only in-training distribution sounds, failing on OOD regime

Link & full story 🧵👇
moonliyp.bsky.social
250801
To be a better husband.
Reposted by Yipeng Li
biorxiv-neursci.bsky.social
Spatial Reorganization of Object Representations in High-Level Visual Cortex Distinguishes Working Memory from Perception https://www.biorxiv.org/content/10.1101/2025.06.29.662186v1
Reposted by Yipeng Li
nblauch.bsky.social
What shapes the topography of high-level visual cortex?

Excited to share a new pre-print addressing this question with connectivity-constrained interactive topographic networks, titled "Retinotopic scaffolding of high-level vision", w/ Marlene Behrmann & David Plaut.

🧵 ↓ 1/n
moonliyp.bsky.social
Great tool!
neurabenn.bsky.social
Precon_all is finally citeable! I’m relieved, pleased, thrilled and all other superlatives to present to you our preprint describing the inner workings of the precon_all pipeline for semi-automated non-human cortical surface reconstruction! www.biorxiv.org/content/10.1... 1/10
moonliyp.bsky.social
Getting back from @vssmtg.bsky.social with unforgettable and fruitful memories - now taking train to Beijing from HongKong, way more relaxing for someone who hates flying like me
moonliyp.bsky.social
Poster 436 this morning for Xieyi’s work on separated and integrated processing of shape and texture in IT cortex!
moonliyp.bsky.social
Just arrived at @vssmtg.bsky.social this year, with newest results from B-Lab. Looking forward to discussion with all friends!
moonliyp.bsky.social
Poster 451 this morning for Baoqi’s work on food representation in macaque brain!
moonliyp.bsky.social
Just arrived at @vssmtg.bsky.social this year, with newest results from B-Lab. Looking forward to discussion with all friends!
Reposted by Yipeng Li
guidomeijer.com
Kilosort4 detects a LOT of neurons, I recorded 15k neurons in one year 🤯 Traditionally, one would curate these detected units to see if they are well isolated single neurons. This is not feasible anymore, so today let's look at three options that are out there to automate this process! 🤖👇
moonliyp.bsky.social
Just arrived at @vssmtg.bsky.social this year, with newest results from B-Lab. Looking forward to discussion with all friends!
moonliyp.bsky.social
Hi Alex, thank you for your interest! the manuscript will be submitted in these days. If we get stuck in the review process (which can be unpredictable), we’ll share the data before formal publication. But at this time I can’t promise a specific timeline
moonliyp.bsky.social
Deeply grateful to all the generous friends for all the invaluable advice and support during the development of our recording techniques!🍺
moonliyp.bsky.social
(6/6) We believe the triple-N dataset serves as both a valuable resource for visual neuroscience and a detailed complement to the NSD fMRI dataset. We are currently finalizing the data organization and description - please stay tuned for the release😁😁😁
moonliyp.bsky.social
(5/6) Our dataset enables comparative analysis of divergent neural coding preference for visual vs semantic features across species.
moonliyp.bsky.social
(4/6) Combined with NSD's fMRI data, this enables new studies of both homology AND differences in high-level visual cortex between macaques and humans.
moonliyp.bsky.social
(3/6) We identified three distinct unit types with unique temporal profiles - tightly linked to image preference and spatial organization patterns.
moonliyp.bsky.social
(2/6) Using fMRI-guided Neuropixels recordings, we targeted category-selective regions (face/body/object/scene/color), collecting >20k units across 59 sessions while macaques viewed NSD shared1000 stimuli. This provides rich single-trial population responses
moonliyp.bsky.social
(1/6) Thrilled to share our triple-N dataset (Non-human Primate Neural Responses to Natural Scenes)! It captures thousands of high-level visual neuron responses in macaques to natural scenes using #Neuropixels.
moonliyp.bsky.social
bought a ‘steady’ pin and tried the most nerve-wracking things I've ever done: inserting a 44mm Neuropixels probe into a guide tube