Завтра в Пушкинском музее. https://pushkinmuseum.art/events/
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Channel
@kaplanalexandr
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Telegram's recommendations · Cite this entry
7,536subscribers
-34 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001831057415 |
|---|---|
| Type | Channel |
| Username | @kaplanalexandr |
| Created | Between 1 October 2022 and 30 September 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 15 September 2026 |
| Measurements held | 13 |
| Confirmed unchanged | 2 times, most recently 15 September 2026 |
| On Telegram | t.me/kaplanalexandr |
Other / unclassifiable — 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 12 September 2026 and assigned it the closest of 31 fixed categories, at 70% 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 |
|---|---|---|
| 11 Sept 2026, 20:36 | 7,536 | -4 |
| 7 Sept 2026, 01:01 | 7,540 | -5 |
| 2 Sept 2026, 04:25 | 7,545 | +7 |
| 29 Aug 2026, 20:36 | 7,538 | +5 |
| 26 Aug 2026, 23:49 | 7,533 | -8 |
| 24 Aug 2026, 01:12 | 7,541 | -8 |
| 20 Aug 2026, 10:53 | 7,549 | -2 |
| 17 Aug 2026, 17:48 | 7,551 | -7 |
| 14 Aug 2026, 17:54 | 7,558 | -1 |
| 10 Aug 2026, 22:17 | 7,559 | -12 |
| 8 Aug 2026, 07:04 | 7,571 | +1 |
| 7 Aug 2026, 09:30 | 7,570 | no change |
| 7 Aug 2026, 09:23 | 7,570 | first reading |
26 posts held, back to 28 May 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 18 pages of Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 28 August 2026 |
|---|---|
| Posts held | 26 (28 May 2026 – 28 August 2026) |
| Views total | 4,072 |
| Reactions total | 134 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 28 Aug 2026, 23:13 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.
Measured directly from 6 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
1,582 reactions across 22 posts, in 6 distinct kinds. The most used accounts for 64.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 1,020 | 64.5% | |
| 👍 | 260 | 16.4% | |
| 🔥 | 239 | 15.1% | |
| 👏 | 51 | 3.22% | |
| 🤔 | 11 | 0.695% | |
| 👎 | 1 | 0.063% |
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 22 of the 26 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 1,582 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 26 most recent posts we hold, published 28 May 2026 to 28 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.
Завтра в Пушкинском музее. https://pushkinmuseum.art/events/
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Психофизиолог Каплан Александр pinned a photo
❗️9 сентября встречаемся на лекции в Санкт-Петербурге! Подробности тут: https://www.pryamaya.ru/aleksandr_kaplan_mozg_kak_arhitektor_real_nosti_09_09_2026
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Вопрос: «Как связаны зеркальные нейроны и эмоции?» #отвечает_профессор_Каплан «Не совсем понятен вопрос, но отвечу так. Зеркальные нейроны — это такие нейроны, которые активируются в ответ не на собственные желания, а в ответ на то, что делает кто-то другой. Например, если я хочу взять стакан, то у меня активируются определенные нейроны в моторной коре. И те же самые нейроны активируются у меня, если я вижу, как др…
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Когнитивные искажения особенно вредны для людей, работающих в науке, бизнесе и оперативной среде, где постоянно приходится принимать множество решений. Одно из таких крупных групповых искажений — эффект доктора Фокса. Суть его ярко показал эксперимент: специально подготовленный актер прочитал лекцию, в которой рассказал полную ерунду — рассказывал о невиданных делах, об открытии неких новых закономерностей. Его выст…
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Иногда мы помним какие-то детали, которых не было. Почему это происходит? Потому что модель, которая сформирована в мозге, зачем-то дописала обстоятельства. Модель подчиняется запросам, потребностям организма и пытается мне помочь, добавляя что-то в картину. Опыт у нее большой, много чего она как бы знает — и дописывает по примеру того, что где-то когда-то было. И вот вы уже уверены, что были здесь, а на самом деле …
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Как во время исследования деятельности мозга монаха ученые могут отделить одну стадию медитации от другой? #отвечает_профессор_Каплан «Это одна из самых больших проблем для экспериментатора. Потому что если монах прервется, чтобы что-то сообщить нам, ему придется начинать всё с начала. Поэтому ничего он не может нам сказать во время медитации. Мы ведем запись процесса от и до: что записали, то записали. Есть некая…
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Ум легко соскальзывает в тревогу и информационный хаос, такова его естественная реакция на окружающую действительность. Но если посмотреть на эти процессы сквозь призму нейрофизиологии, многое встает на свои места. Если понимать, как устроены нейронные связи и чем они «живут», то причины наших эмоций, усталости или реакций перестают быть тайной. Появляется фундаментальная опора: возможность осознанно менять привычны…
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«Виртуальная реальность — это созданный техническими средствами мир, передаваемый человеку через его ощущения. То есть наши собственные ощущения дают нам опознавательный знак, что мы находимся в какой-то реальности. В «Матрице» ощущения создавали так: кабель всунули в голову и создают эти ощущения через мозг. Можно создавать их через внешние чувства — очки, шлемы виртуальной реальности. Но и простое кино ее создает:…
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«Существует ряд ключевых экспериментов, демонстрирующих, как мозг формирует готовность к действию — например, к тому, чтобы взять стакан. Сам человек ещё не догадывается о собственном намерении, но приборы уже фиксируют: рука вот-вот поднимется. Возникает вопрос: кто же в действительности это делает? Не стоит поддаваться заблуждениям: это совершаем именно мы, поскольку потребность взять стакан зарождается в нашей со…
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«Для чего нам нужен мозг? Мозг — это орган, специализирующийся на адаптации организма к среде обитания. Вот его главная функция. А не для того, чтобы, как обычно мне отвечают, думать: где-то и не надо думать, где-то надо глазами увидеть и быстро среагировать. Мозг умеет собирать от органов чувств информацию, ее перерабатывать и создавать новое поведение. Всё это — для адаптации к среде обитания, потому что те, кто л…
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Психофизиолог Каплан Александр pinned Deleted message
Showing the 12 most recent of 26 posts we hold for @kaplanalexandr. 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.
@kaplanalexandr edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
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 4 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.
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.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 11 September 2026 — this entry's latest reading, not the date you are reading this.
“Психофизиолог Каплан Александр” (@kaplanalexandr), 7,536 subscribers as measured 11 September 2026. Telegram Register, tgregister.com/channel/kaplanalexandr.
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.