Channel
@neuroinklab
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Cite this entry
502subscribers
+11 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002308026298 |
|---|---|
| Type | Channel |
| Username | @neuroinklab |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 19 September 2026 |
| Measurements held | 6 |
| Confirmed unchanged | 1 time, most recently 19 September 2026 |
| On Telegram | t.me/neuroinklab |
Science — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 21 September 2026 and assigned it the closest of 31 fixed categories, at 99% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 19 Sept 2026, 13:38 | 502 | -1 |
| 11 Sept 2026, 19:21 | 503 | +4 |
| 27 Aug 2026, 21:45 | 499 | +7 |
| 20 Aug 2026, 19:01 | 492 | +1 |
| 6 Aug 2026, 18:47 | 491 | no change |
| 6 Aug 2026, 03:53 | 491 | first reading |
20 posts held, back to 25 February 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 6 August 2026. An engagement rate over an empty window would be a number about nothing.
17 reactions across 8 posts, in 2 distinct kinds. The most used accounts for 94.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 16 | 94.1% | |
| 🔥 | 1 | 5.88% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 11 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 17 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 25 February 2026 to 6 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
A multimodal approach for visualizing and identifying electrophysiological cell types in vivo Abstract Neurons of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological recordings can measure the activity of many neurons simultaneously, identifying cell types during these experiments remains difficult. Here we present PhysMAP, a…
مطالعه حرکات ریز چشم تاریخچهای غنی دارد؛ از نخستین اشارههای ثبتشده در سال ۱۵۹۹ توسط Du Laurens تا مطالعات جدیدتر پژوهشگران برجسته این حوزه، مانند Ziad Hafed و Richard Krauzlis. با وجود تفاوت در روشها و تفسیرها، تقریبا همهی این پژوهشها در یک نکته اشتراک دارند: چشم حتی هنگام تثبیت نگاه نیز کاملا بیحرکت نمیماند. در این مطلب، نگاهی کوتاه به تاریخچه مطالعه Fixational Eye Movements خواهیم داشت. برای نخستین با…
🔥1
Conscious and nonconscious thought: Insights from the neuroscience of decision-making Abstract Conscious thought is often treated as a special class of neural processing, distinct from the nonconscious computations that guide most behavior. Here I offer a different perspective, grounded in the neuroscience of decision-making. I argue that conscious thought arises not from a unique mechanism but from a distinctive us…
Much higher covariation with foveation timing by superior colliculus than primary visual cortical neuronal activity Summary The superior colliculus (SC) is a potent driver for orienting movements, but it also possesses short-latency visual responses. Even though such responses largely derive from direct primary visual cortical (V1) input, their relationship to V1 activity has never been analyzed with the same experi…
**De novo fast motion computation in the primate visual cortex** Summary Objects move through space and time, generating sequential visuotopic activations in all sighted animals, leading to velocity perception defined by direction and speed. The primary visual cortex (V1) only encodes directionality at low speed, yet we effortlessly perceive velocities ranging from 0.25°/s to 500°/s. To resolve this paradox, we reco…
Experience reorganizes content-specific memory traces in macaques Abstract Memory formation relies on the reorganization of neural activity patterns during experience that persist in subsequent sleep. How these processes promote learning while preserving established memories remains unclear. Here we recorded neural ensemble activity from the hippocampal and associated regions in freely moving macaques as they recall…
Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex Abstract Temporal continuity of object identity is a feature of natural visual input and is potentially exploited - in an unsupervised manner - by the ventral visual stream to build the neural representation in inferior temporal (IT) cortex. Here, we investigated whether plasticity of individual IT…
Neural traveling waves in cortex: Network mechanisms and potential roles in neural computation Summary First discovered in anesthetized animals, neural traveling waves (nTWs) have now been observed throughout the brains of awake animals, where they influence neural excitability and behavior. nTWs can arise intrinsically in ongoing network activity or be triggered by sensory input and behavioral events. By structurin…
Rarely categorical, highly separable representations along the cortical hierarchy Abstract A long-standing debate in neuroscience concerns whether individual neurons are organized into functionally distinct populations that encode information differently (categorical representations) and the implications for neural computation. Here we systematically analysed how cortical neurons encode cognitive, sensory and moveme…
❤4
Visuomotor flexibility is embedded in the topography of frontal cortex Abstract Visual selection and saccade targeting are not obligatorily coupled, allowing a stimulus at one location to guide an eye movement toward another. This is mediated by frontal oculomotor cortex, where maps of visual and motor space converge, yet whether the topographic relationship between these maps itself contributes to this flexibility …
❤1
KIASORT: Knowledge-Integrated Automated Spike Sorting for Geometry-Free Neuron Tracking Abstract Modern high-density neural recordings demand spike-sorting algorithms that can handle diverse probe geometries and complex, neuron-specific drift, yet existing methods often rely on rigid geometric assumptions and one-dimensional drift models. Here, we introduce KIASORT (Knowledge-Integrated Automated Spike Sorting), a g…
❤2
Reading and writing neural activity with Neuropixels Opto probes High-density electrophysiology devices allow neuroscientists to observe spikes from large populations of neurons, and optogenetics allows them to drive or suppress those spikes. We show that a single device can combine these two capabilities, providing a high-resolution means to both read and write neural activity in the living brain. #Josh_Siegle #Ma…
Showing the 12 most recent of 20 posts we hold for @neuroinklab. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 19 September 2026 — this entry's latest reading, not the date you are reading this.
“NeuroInk Lab” (@neuroinklab), 502 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/neuroinklab.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.