Thank you to the authors at the UTHealth Tandon Lab for your work!
cc: Tessy Thomas, Jinglong Li, @gregoryhickok.bsky.social, Xaq Pitkow, Nitin Tandon

❔3️⃣: In the multi-subject REO analysis that showed robustness to loss of SMC electrodes (Fig. 4b), did you investigate which non-SMC regions contributed the most in the absence of SMC electrodes? Could this robustness be driven by auditory feedback signals when SMC electrodes are removed?

❔2️⃣: There is variability in PER gains as the transfer learning is applied between top-performing training subjects and inference subjects (Fig. 5c). Is this variability again driven by the amount of coverage correlation between training and inference subjects (as in Fig. 3e) or something else?

❔1️⃣ (con’t): For example, maybe models with training sets that more widely sample distributed speech network areas are more robust to regional exclusion, while training sets composed of focal coverage in a singular region may not be as generalizable.

❔1️⃣ : In multi-subject models, is there any effect of electrode coverage across training patients on PER in inference patients?

🤍3️⃣: The cross-task transfer learning analysis (tongue-twister to TIMIT) is important for understanding changes in transfer learning performance as task structure changes.

🤍2️⃣: The comparison of multiple network transfer strategies (full, readout, recurrent) provides useful insight into necessary model architectures to capture shared speech representations.

🤍1️⃣: The regional exclusion analysis is an interesting way to investigate robustness to loss of information from important cortical areas.

Transfer learning via distributed brain recordings enables reliable speech decoding - Nature Communications Speech brain-computer interfaces face challenges scaling across individuals with different brain organization. Using minimally invasive recordings from 25 patients, the authors developed transfer lear...

They find that that models pre-training models on high-performing patients reduces predicted phoneme error rate and makes models more robust to the exclusion of electrodes in important regions. This 🧵 explores our thoughts (🤍 & ❔). www.nature.com/articles/s41...

Last week, Zac Spalding (@zspald.bsky.social‬, 4th year PhD student, @dukeubme.bsky.social‬) presented Aditya Singh and colleagues’ 2025 paper on multi-regional, multi-subject neural speech decoding using from SEEG electrodes.

Thank you to the authors @ucsanfrancisco.bsky.social , @neurosurgucsf.bsky.social, and
@changlabucsf.bsky.social for your
work, and we look forward to following its progress!

❔2️⃣: How do the phonological representation profiles of early-activating electrodes diverge across these two regions?

❔3️⃣: How does individual variability in structural connectivity account for the variance in
early frontal speech representations?

🤍3️⃣: The usage of white matter and resting-state connectivity to identify underlying neural pathways adds supporting structural evidence for neural computations

❔1️⃣: How does the prevalence of early-response electrodes differ between the frontal lobe and the STG?

🤍2️⃣: The integration of power and encoding analyses to pinpoint onset responses and
representational content captures more complete neural representation profiles.

Parallel encoding of speech in human frontal and temporal lobes - Nature Communications Whether high-order frontal lobe areas receive raw speech input in parallel with early speech areas in the temporal lobe is unclear. Here, the authors show that frontal lobe areas get fast low-level sp...

This 🧵 explores our thoughts (& ❔) nature.com/articles/s41...

🤍1️⃣: The employment of a data-driven approach to map frontal speech-aligned electrodes
helps probe latent variants in electrophysiological data.

Last week, Baishen Liang (postdoctoral associate) led a discussion on a recent ieeg
speech paper from the Chang Lab by Patrick W. Hullett and colleagues on the frontal
and temporal parallel cortical speech processing pathways.

Very excited to have you here and looking forward to working with you :)

Thank you to the authors
@princetonupress.bsky.social Neuro
for your work, and we look forward to following more of it!
CC:
@timbuschman.bsky.social ,
@tafazolisina.bsky.social

❔3⃣: Do tasks of differing complexity occupy differing capacity in working memory? Or are complex tasks abstracted away such that they occupy the same amount of working memory capacity as simple tasks?

❔2⃣: When and where in the brain determines which neural populations to amplify and suppress to implement any given task? Why?

❔1⃣ (con't): What is the elementary unit of a task? Under a programming analogy, what is/are the simplest function(s) that the brain represents and combines to create more complex functions? Conversely, what is the most complex possible task—our search for meaning?

❔1⃣: The color/shape categorization and response direction subtasks can be broken into even smaller subtasks (e.g., look at fixation cross, remember what color red & green are or what a bunny or a tee is, look at the corner of a box). Is this turtles all the way down (and up)?

🩶2⃣: The research question is simple, intuitive, and practical yet very robustly tested
🩶3⃣: The cross-decoding analyses were a nice way of assessing whether sensorimotor representations transferred across tasks.

🩶1⃣: Given the lack of an S2 task, this paper elegantly minimized the learning load on the monkeys while still testing their core question of whether tasks are composed of shared sensorimotor representations

Last week, Jim Zhang (4th year PhD student,
@DukeBrain
) presented Sina Tafazoli’s paper on building compositional tasks with shared neural subspaces. This 🧵 explores our thoughts (🤍 & ❔)

Come by tomorrow morning to see Baishen's work on verbal working memory!

Come by this morning to see Areti's poster!

At #Sfn2025 ?
Come see some of the lab's posters this afternoon!

Stop by to say hello and see some great science!
#Sfn2025 #Neuroscience #neuroskyence

Lastly (not least):

Wed. Nov 19 8am-12pm: 411.11 / MM10

Sensory-motor mechanisms for verbal working memory*

Postdoc Baishen Liang will be presenting his work on sensory-motor transformations for vWM
@gregoryhickok.bsky.social

*Also presenting at APAN