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Cogan Lab @coganlab.bsky.social · 26d

Come by tomorrow morning to hear about verbal working memory!

Cogan Lab @coganlab.bsky.social · 28d

Saturday Sept. 13 11am-12:30pm, Poster Session C

C54: Baishen Liang (Postdoctoral Associate) will be presenting his work on sensory-motor mechanisms for verbal working memory.

Hope to see you all there!

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Cogan Lab @coganlab.bsky.social · 26d

Stop by this afternoon to see some intracranial speech decoding in the hippocampus and to say hello!

Cogan Lab @coganlab.bsky.social · 28d

Friday Sept 12 4:30pm-6:00pm, Poster Session B

B70: Yuchao Wang (Rotation CNAP PhD Student) will be presenting his work on auditory pseudoword decoding in the hippocampus.

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Cogan Lab @coganlab.bsky.social · 28d

Saturday Sept. 13 11am-12:30pm, Poster Session C

C54: Baishen Liang (Postdoctoral Associate) will be presenting his work on sensory-motor mechanisms for verbal working memory.

Hope to see you all there!

Cogan Lab @coganlab.bsky.social · 28d

Friday Sept 12 4:30pm-6:00pm, Poster Session B

B70: Yuchao Wang (Rotation CNAP PhD Student) will be presenting his work on auditory pseudoword decoding in the hippocampus.

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Cogan Lab @coganlab.bsky.social · 28d

Coming to DC for SNL later this week?

Come check out our posters on speech decoding and verbal working memory using intracranial recordings!

@snlmtg.bsky.social
#SNL2025

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Cogan Lab @coganlab.bsky.social · 29d

❔3️⃣: In Figs. 4 and 5, do you obtain similar results if you operate directly on the spike trains instead of on the PCA-reduced spike trains? Why is PCA necessary first?

Thank you to the authors for your work!
cc: Alexis Arnaudon, Mauricio Barahona, Pierre Vandergheynst

Cogan Lab @coganlab.bsky.social · 29d

If separate animals were treated as separate manifolds with an embedding-agnostic MARBLE, would you still expect an informative latent space to be learned without any need for post-hoc alignment?

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Cogan Lab @coganlab.bsky.social · 29d

❔2️⃣: It seems that a linear transformation between MARBLE representations of different animals was necessary because the same information is present in the latent space but not necessarily with the same ordering... (con't)

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Cogan Lab @coganlab.bsky.social · 29d

❔1️⃣: It is stated that non-neighbors (both within and across manifolds) are negative samples (mapped far) during the contrastive learning step. Does treating non-neighbors within and across manifolds as similarly “distant” lead to less interpretability of larger distances in latent space?

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Cogan Lab @coganlab.bsky.social · 29d

🤍3️⃣: The comparisons to state-of-the-art latent dynamical systems models are great for properly contextualizing the performance of MARBLE.

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Cogan Lab @coganlab.bsky.social · 29d

🤍1️⃣: The initial proximity graph is a clever way to define distance and neighborhoods between inputs that can be used for downstream training.
🤍2️⃣: The rotation invariance is important and likely useful for extracting shared latent representations from systems with minor differences.

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Cogan Lab @coganlab.bsky.social · 29d

They find that MARBLE successfully decomposes complex dynamical activity from spike trains into informative and easily decodable latent representations. This 🧵 explores our thoughts (🤍 & ❔). www.nature.com/articles/s41...

MARBLE: interpretable representations of neural population dynamics using geometric deep learning - Nature Methods

MARBLE uses geometric deep learning to map dynamics such as neural activity into a latent representation, which can then be used to decode the neural activity or compare it across systems.

www.nature.com

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Cogan Lab @coganlab.bsky.social · 29d

Last week, Zac Spalding (@zspald.bsky.social‬, 4th year PhD student, @dukeubme.bsky.social‬) presented Adam Gosztolai, Robert Peach, and colleagues’ 2025 paper on MARBLE, a method for finding interpretable latent representations of neural dynamics.

MARBLE: interpretable representations of neural population dynamics using geometric deep learning - Nature Methods

MARBLE uses geometric deep learning to map dynamics such as neural activity into a latent representation, which can then be used to decode the neural activity or compare it across systems.

www.nature.com

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Reposted by Cogan Lab

Duke Institute for Brain Sciences @dukebrain.bsky.social · Aug 27

Exciting work by PhD student Zac Spalding @zspald.bsky.social and multiple labs across departments at Duke! A combination of human electrophysiology, modeling/decoding, and cognitive concepts to improve BCI for speech in patients. 👏 🧠📈

Cogan Lab @coganlab.bsky.social · Aug 25

We’re happy to present @zspald.bsky.social 's work on shared neural representations of speech production across individuals! We find that patient-specific data can be aligned to a shared space that preserves speech information, enabling cross-patient speech BCIs.
www.biorxiv.org/content/10.1...

Aligned neural space for speech production allows for improved decoding in micro-ECOG patients for BCI

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Cogan Lab @coganlab.bsky.social · Aug 26

From Cogan Lab Journal Club with @zspald.bsky.social
these decomposition acronyms are getting out of hand!

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Cogan Lab @coganlab.bsky.social · Aug 25

We’re happy to present @zspald.bsky.social 's work on shared neural representations of speech production across individuals! We find that patient-specific data can be aligned to a shared space that preserves speech information, enabling cross-patient speech BCIs.
www.biorxiv.org/content/10.1...

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Cogan Lab @coganlab.bsky.social · Aug 19

❔3️⃣: Have the authors performed unique syllable-type decoding in other regions? Which regions might be responsible for more detailed/specific motor sequence planning?

Thank you to the authors @changlabucsf.bsky.social for your great work, and we look forward to following more of it!

Cogan Lab @coganlab.bsky.social · Aug 19

❔2️⃣: Given that the specific content of a sequence from pre-speech activity can not be reliably decoded, how do you interpret the nature of the motor plan in the mPrCG?

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Cogan Lab @coganlab.bsky.social · Aug 19

❔1️⃣: Why do you think the sustained activity in the vPrCG is modulated by articulatory complexity, yet it is unable to predict reaction time (RT)?

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Cogan Lab @coganlab.bsky.social · Aug 19

🤍3️⃣: The paper does an excellent job of connecting a basic science finding to a real-world clinical disorder. The demonstration that mPrCG stimulation produces errors that are “consistent with those made in AOS, offering significant insights for both researchers and clinicians.

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Cogan Lab @coganlab.bsky.social · Aug 19

🤍2️⃣: I also like the clear narrative structure: identifying a network → defining its function → linking it to behavior → providing causal evidence.

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Cogan Lab @coganlab.bsky.social · Aug 19

🤍1️⃣: I appreciate that they were able to establish a causal role for the mPrCG through electrical stimulation.

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Cogan Lab @coganlab.bsky.social · Aug 19

Last week, Nanlin Shi (Postdoctoral Associate) presented Jessie Liu and colleague’s latest work on speech encoding in the middle Precentral Gyrus (mPrCG). This 🧵 explores our thoughts (🤍 & ❔)
www.nature.com/articles/s41...

Speech sequencing in the human precentral gyrus - Nature Human Behaviour

Liu et al. examine the role of sustained neural activity in the planning and production of speech sequences, revealing a key role for the middle precentral gyrus.

www.nature.com

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Cogan Lab @coganlab.bsky.social · Jul 22

Thank you to the authors at UC Berkeley Psychology and Neuroscience for your work, and we look forward to following more of it!

cc: @ucberkeleyofficial.bsky.social

Cogan Lab @coganlab.bsky.social · Jul 22

❔3️⃣: In Fig. 2C, it looks like MFG has early low-frequency activity during the approach period that dissipates about 500ms before the decision to turn around. Could this be preparatory activity that reflects participants’ plans for the trial, or predictive of how many dots they decide to eat?

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