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Xianhui He

@xianhuihe.bsky.social

何贤辉 Oxford DPhil student at the Staresina lab, go crazy for brain🧠 and volleyball🏐

10 Posts24 Follower 25 Folgt

PLOS Biology @plosbiology.org · 4mo ago

How do experiences reshape our internal representations of the world? @bstaresina.bsky.social &co show that learning sequential experiences reshapes how the #brain represents what we see; a post-learning nap strengthens these predictive changes @plosbiology.org 🧪 plos.io/4dJGwMC

Emergence of Successor Representations and Experimental Design. Top: Example of how sequence learning and sleep might change neural representations. Upon encountering a Welsh Corgi, the brain primarily represents the current stimulus entity. If the Corgi is part of a recurring temporal sequence (Corgi → Girl → House), subsequent stimuli (Girl and House) might be integrated into the Corgi representation. Post-learning sleep might provide an opportunity for the brain to replay learned experiences and thereby further strengthen successor representations. Upon post-sleep exposure to a Corgi image (right), brain activation patterns might reflect both the current stimulus (Corgi) as well as learned successors (Girl, House). Faded images indicate weaker representations. Middle: Timeline of the experiment. Participants first completed a perceptual task, followed by a sequence learning task (Memory Arena). Memory for the learned sequence was then assessed both before and after a period of sleep. Finally, participants completed the perceptual task again. Bottom left: Memory Arena sequence design. Participants (N = 26) were tasked with learning the spatiotemporal structure of 50 images. These images belonged to five distinct categories (letter strings, scenes, objects, faces, and body parts) and were organized into 10 subsequences of five images each, following one of two fixed category orders: (i) letter string, scene, object, face, or (ii) object, scene, letter string, face, with body part images randomly inserted to obscure the primary category sequences. The two subsequence types were counterbalanced across participants. Bottom right: Memory Arena location design. The Arena was spatially organized into five principal ‘slices’, with each slice corresponding to one of the five main image categories.

Marcel S. Kehl @mskehl.bsky.social · 5mo ago

🔔PREPRINT: Sleep ripples drive single-neuron reactivation for human memory consolidation 1/9: How does sleep support human memory consolidation? To test this, we recorded hundreds of neurons in the human medial temporal lobe (MTL) across learning, wakefulness, and sleep. doi.org/10.64898/202...

doi.org

Marcelo Mattar @marcelomattar.bsky.social · last yr.

Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s415...

Discovering cognitive strategies with tiny recurrent neural networks - Nature

Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the underlying cog...

doi.org

Xianhui He @xianhuihe.bsky.social · last yr.

How does our brain learn that thunder follows lightning? We don't just remember two separate events; we build a predictive model to anticipate the world. My research dives into this very question: how we learn and predict the order of events. 🧵👇 1/9 #neuroscience #memory #sleep

Katrijn Schruers @katschruers.bsky.social · last yr.

What a great start to #CNS2025! @manqisha.bsky.social kicked of the first session with her Data Blitz on ‘Coupled sleep rhythms in the human hippocampus support memory consolidation’

Katrijn Schruers @katschruers.bsky.social · last yr.

The Staresina Lab is ready for CNS 2025 in Boston!🇺🇸 If you are around come and check out our work at the following poster sessions: Poster Session C Poster C16: Coupled sleep rhythms in the human hippocampus support memory consolidation - Sha, Manqi