Telegram RegisterThe public register of Telegram

Channel

Нейроинтерфейсы

@bci_ru

On this record: Also posting the same content · Growth · Engagement · Reactions · Posts · Citations · Cite this entry

6,385subscribers

+9 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001634978355
TypeChannel
Username@bci_ru
Created27 March 2022measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/bci_ru

Also posting the same content

This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.

Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.

Growth

6,3736,3866,379.56 August 2026 — 6,376 subscribers7 August 2026 — 6,373 subscribers10 August 2026 — 6,386 subscribers12 August 2026 — 6,385 subscribers6,3856 August 202612 August 2026
4 measurements spanning 7 days, net +9. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 6,371–6,388 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 21:286,385-1
10 Aug 2026, 02:136,386+13
7 Aug 2026, 01:456,373-3
6 Aug 2026, 06:196,376first reading

Engagement

29 posts held, back to 26 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 9 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
11.6%
avg views ÷ 6,385 subscribers
Avg views / post
738
29 posts measured
Reaction rate
1.15%
reactions ÷ views · ER floor
Posts in window
29
of 29 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 27 of 29 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 11 August 2026
Posts held29 (26 July 202611 August 2026)
Views total21,414
Reactions total228
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 03:13 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

Reaction mix

220 reactions across 26 posts, in 15 distinct kinds. The most used accounts for 30.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍6830.9%
6429.1%
❤‍🔥2410.9%
😁219.55%
🔥135.91%
🤯73.18%
👌52.27%
👎52.27%
🕊31.36%
🤝31.36%
🤔20.909%
🥴20.909%
💘10.455%
💯10.455%
🥰10.455%

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 27 of the 29 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 228reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 29 most recent posts we hold, published 26 July 2026 to 11 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.

Recent posts

11 Aug 2026, 21:46 UTC158 views2 reactionsread 12 August 2026
Forwarded from @neuronovostiPhoto

Могут ли биомаркеры боли заменить опыт пациента? В исследовании, опубликованном в Nature Neuroscience, представили критический анализ использования нейровизуализации и биомаркеров для диагностики боли. Главный аргумент исследователей: невозможно создать объективный тест, который доказал бы наличие или отсутствие боли у конкретного человека и смог ее объективно измерить, а биомаркеры могут быть лишь вспомогательным и

2

Signed Sergei Shishkin

11 Aug 2026, 21:21 UTC224 views3 reactionsread 12 August 2026

В постере даётся обзор шести наших исследований, но их было уже девять. Анализ результатов двух из них (Vasilyev et al. - исследование физиологичесиких механизмов квази с использованием МЭГ, Svirin et al. - риалтайм ИМК с тензодатчиком и новыми движениями) ещё не совсем завершён, а вот исследование, где испытуемые делали квази или представляли движения на фоне саккад, я, честно говоря, просто забыл. Хотя постер о нем

🔥21

Signed Sergei Shishkin

11 Aug 2026, 20:30 UTC325 views7 reactionsread 12 August 2026

На XXV Съезде Физиологического общества им. И.П. Павлова, который сейчас проходит в Москве, завтра (в среду, 12 августа) будет два постера про квазидвижения: - Шишкин С.Л., Яшин А.С., Иванов Л.А., Кондур А.А., Бобров Д.А., Слюнькова Е.В., Габузов Г.Г., Лукьянов Е.С., Шевцова Ю.Г., Свирин Е.П., Васильев А.Н., Бобров П.Д., Котов С.В. Квазидвижения: от изучения фундаментальных свойств до трансляции в клинику. — PDF см.

7

Signed Sergei Shishkin

9 Aug 2026, 17:19 UTC623 views27 reactionsread 12 August 2026
Forwarded from @neuronovostiPhoto

Найден участок мозга для общей работы рук и рта во время еды Американские нейробиологи обнаружили в коре мозга мышей специализированную зону с двумя отдельными типами нервных клеток, которая координирует совместную работу рук и рта во время еды. Один тип клеток отвечает за силу и точность самих движений – например, за то, с каким усилием рука подносит еду ко рту. Второй синхронизирует движения рук и челюсти по време

❤‍🔥17🤯7👍21

Signed Sergei Shishkin

7 Aug 2026, 16:52 UTC783 views7 reactionsread 12 August 2026
Photo

Большое спасибо за репост! Для поддержки работы необходимо перейти по ссылке и нажать на кнопку Upvote 🫶

5👍2

Signed Sergei Shishkin

7 Aug 2026, 16:29 UTC757 views7 reactionsread 12 August 2026

Ссылка на Hugging Face для тех, кто не увидел: https://huggingface.co/papers/2608.01481

👍43

Signed Sergei Shishkin

7 Aug 2026, 14:26 UTC695 views20 reactionsread 12 August 2026

⬆️⬆️⬆️⬆️ Поддержите на Hugging Face крутую статью по интерпретируемому (!) декодированию воспринимаемой речи по МЭГ коллег из Центра биоэлектрических интерфейсов ВШЭ! Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval by Ilia Semenkov, Daria Kleeva, Ivan Dakhtin, Zarina Maksudova, Alex Ossadtchi

👍163😁1

Signed Sergei Shishkin

7 Aug 2026, 14:22 UTC672 views9 reactionsread 12 August 2026
Forwarded from @neuroexmachinaPhoto

🔊🧠 Декодирование воспринимаемой речи из МЭГ: может ли декодер быть точным и одновременно интерпретируемым? Сегодня хочу рассказать о нашем новом препринте про интерпретируемое декодирование речи из МЭГ. 🎧 Задача Есть МЭГ 27 человек, слушавших четыре разные истории. Мы нарезаем МЭГ и аудио на 3-секундные сегменты и по каждому сегменту МЭГ ищем, какой из 1005 тестовых аудиофрагментов человек слушал. Но это не прост

👍53🔥1

Signed Sergei Shishkin

6 Aug 2026, 19:07 UTC788 views25 reactionsread 12 August 2026

Михаил Лебедев по-английски рассказывает о том, как хорошо летают голуби-биодроны, которым помогает ИИ. Его внимательно слушает голубь, но никуда не летит, так как находится в клетке: https://youtu.be/DZ-BI_YbR5U

😁18👎4🕊3

Signed Sergei Shishkin

4 Aug 2026, 15:39 UTC795 views16 reactionsread 12 August 2026
Forwarded from @ivoryzooPhoto

#кросскультурная_коммуникация Но есть и хорошие новости - кажется, человечество-таки пережило 4 августа 2026 не совсем так, как это предвидел гениальный золотоглазый внук ведьмы Рэй Бредбери в своих «Марсианских хрониках» (самым младшим научным сотрудникам в этом канале дешифруем: одном из самых знаменитых научно-фантастических рассказов-антиутопий ХХ века). Точно стоит перечитать, если вдруг вы еще не. Пять минут

15🥴1

Signed Sergei Shishkin

4 Aug 2026, 12:09 UTC774 viewsread 12 August 2026
Forwarded from @liftfeed

Про туннельные нанотрубки в ЦНС, еще один канал связности клеток мозга, помимо синапсов, э/полей и диффузии. ТНТ могут переносить молекулы и органеллы между нейронами, играя роль в гомеостазе и различных нарушениях, что делает их потенциальной мишенью для терапии. — Текущая картина и открытые вопросы. Небольшой, но оч. интересный обзор. “В более широком смысле, ТНТ могут заставить переосмыслить сам коннектом. Одна и

Signed Sergei Shishkin

4 Aug 2026, 12:06 UTC593 views7 reactionsread 12 August 2026
Forwarded from @neuronovostiPhoto

Магнитные наночастицы вместо электрода Российские учёные из Сколтеха, Института цитологии и генетики СО РАН и других научных центров научились регулировать частоту дыхания и сердечных сокращений мыши при помощи беспроводной стимуляции блуждающего нерва. Источником электростимуляции послужили нетоксичные магнитные микрочастицы, которые играли роль крошечных электродов в нервной ткани. Этот активно исследуемый подход

👍7

Signed Sergei Shishkin

Showing the 12 most recent of 29 posts we hold for @bci_ru. 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.

Citation-graph rank

Citation-graph rank — 157,245 of 1,169,250entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

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.

Mentions

Named by 6 registered channels — 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.

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.

Cite this entry

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 12 August 2026 — this entry's latest reading, not the date you are reading this.

“Нейроинтерфейсы” (@bci_ru), 6,385 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/bci_ru.

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.