Coming to SfN and want to hear about intracranial neural recordings during speech and cognition? Stop by our posters to hear about our science and to chat!

CLcoganlab.bsky.socialOct 4, 2024 12:29am

Coming to Chicago for SfN? Interested in intracranial EEG and speech and cognition? Come see the lab’s posters!

Last week, Jim Zhang (3rd year PhD student, @dukebrain.bsky.socialdoi.org/10.1038/s414...

Human stereoEEG recordings reveal network dynamics of decision-making in a rule-switching task - Nature Communications

How sensory evidence is transformed into motor output is not fully understood. Here, the authors use stereoEEG recordings during a rule-switching task to reveal network dynamics of decision-making.

Last week, Ahmed Hadwan (3rd Year Undergraduate student, Duke Kunshan University) presented Alex Silva and colleagues’ new paper on shared cortical articulatory representations between languages for BCI. This 🧵 explores his thoughts (🤍 & ❔) www.nature.com/articles/s41...

A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages - Nature Biomedical Engineering

Multilingual articulatory representations in the speech-motor cortex of a participant with vocal-tract and limb paralysis enabled the development of a bilingual speech neuroprosthesis.

Last week, Aaron Earle-Richardson (Research Tech) presented Arthur Pellegrino and colleague’s new paper on Dimensionality reduction beyond neural subspaces with slice tensor component analysis. This 🧵 explores his thoughts (🤍 & ❔) www.nature.com/articles/s41...

Dimensionality reduction beyond neural subspaces with slice tensor component analysis - Nature Neuroscience

Neural activity does not always lie in a low-dimensional subspace. The authors extend this classic view to show that task-relevant information is distributed across multiple covariability classes and ...

This week, Nicole Liddle (Sr. Clinical Research Specialist) presented @lauragwilliams.bsky.socialwww.biorxiv.org/content/10.1...

Hierarchical dynamic coding coordinates speech comprehension in the brain

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

This week, Jim Zhang (2nd year PhD student, @dukebrain.bsky.socialwww.nature.com/articles/s41...

Ramping dynamics and theta oscillations reflect dissociable signatures during rule-guided human behavior - Nature Communications

The authors show that neuronal populations in the human prefrontal-motor network interact via two discernible communication modes – ramping dynamics and neural oscillations. These modes operate in con...

Last week, Ahmed Hadwan (3rd Year Undergraduate student at Duke Kunshan University) presented Ariel Goldstein and colleagues’ new paper on shared geometrical patterns between contextual linguistic embeddings and brain embeddings. This 🧵 explores our thoughts (🤍 & ❔) www.nature.com/articles/s41...

Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns - Nature Communications

Here, using neural activity patterns in the inferior frontal gyrus and large language modeling embeddings, the authors provide evidence for a common neural code for language processing.

Message me if you are interested/want to know more!

Last week, Zac Spalding (2nd year BME PhD student) presented Gallego-Carracedo et al.’s 2022 paper investigating the relationship between latent dynamics of neural populations and local field potentials (LFPs) during movement. [cont.]

Last week, undergraduate sophomore Raymond Xiong presented YuanningLi and colleagues’ new paper on using state-of-the-art deep neural network (DNN) models to investigate the neuro-mechanisms of speech perception. This 🧵 explores our thoughts (🤍 & ❔) www.nature.com/articles/s41...

Dissecting neural computations in the human auditory pathway using deep neural networks for speech - Nature Neuroscience

Using direct intracranial recordings and modern speech AI models, Li and colleagues show representational and computational similarities between deep neural networks for self-supervised speech learnin...