Neil Burgess: How your brain tells you where you are

121,263 views ใƒป 2012-02-06

TED


์•„๋ž˜ ์˜๋ฌธ์ž๋ง‰์„ ๋”๋ธ”ํด๋ฆญํ•˜์‹œ๋ฉด ์˜์ƒ์ด ์žฌ์ƒ๋ฉ๋‹ˆ๋‹ค.

๋ฒˆ์—ญ: Woo Hwang ๊ฒ€ํ† : Bianca Lee
00:15
When we park in a big parking lot,
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์•„์ฃผ ํฐ ์ฃผ์ฐจ์žฅ์— ์ฐจ๋ฅผ ์ฃผ์ฐจํ•  ๋•Œ,
00:17
how do we remember where we parked our car?
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์ฐจ๋ฅผ ์–ด๋””์— ์„ธ์›Œ ๋‘์—ˆ๋Š”์ง€ ์–ด๋–ป๊ฒŒ ๊ธฐ์–ตํ• ๊นŒ์š”?
00:19
Here's the problem facing Homer.
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์—ฌ๊ธฐ ํ˜ธ๋จธ์—๊ฒŒ ๋‹ฅ์นœ ๋ฌธ์ œ๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค.
00:22
And we're going to try to understand
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ํ˜ธ๋จธ์˜ ๋จธ๋ฆฌ์†์—์„œ ๋ฌด์Šจ ์ผ์ด ์ผ์–ด๋‚˜๋Š”์ง€๋ฅผ
00:24
what's happening in his brain.
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์•Œ์•„๋ณด๋„๋ก ํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค.
00:26
So we'll start with the hippocampus, shown in yellow,
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๋…ธ๋ž€์ƒ‰์œผ๋กœ ํ‘œ์‹œ๋œ ๊ธฐ์–ต์„ ๋‹ด๋‹นํ•˜๋Š” ๊ธฐ๊ด€์ธ
00:28
which is the organ of memory.
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์ธก๋‘์—ฝ์˜ ํ•ด๋งˆ์—์„œ ์‹œ์ž‘ํ•ด๋ณด์ฃ .
00:30
If you have damage there, like in Alzheimer's,
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์•Œ์ธ ํ•˜์ด๋จธ ๋ณ‘์ฒ˜๋Ÿผ ์ด ๋ถ€๋ถ„์„ ์†์ƒ์ž…๊ฒŒ ๋œ๋‹ค๋ฉด,
00:32
you can't remember things including where you parked your car.
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์—ฌ๋Ÿฌ๋ถ„์ด ์ฐจ๋ฅผ ์ฃผ์ฐจํ•œ ์œ„์น˜๋ฅผ ํฌํ•จํ•ด์„œ ์•„๋ฌด๊ฒƒ๋„ ๊ธฐ์–ต ํ•  ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค.
00:34
It's named after Latin for "seahorse,"
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์ƒ๊ธด๋ชจ์–‘๋„ ๋น„์Šทํ•ด์„œ "ํ•ด๋งˆ"๋ผ๋Š”
00:36
which it resembles.
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๋ผํ‹ด์–ด์—์„œ ๋”ฐ์˜จ ์ด๋ฆ„์ด ๋ถ™์—ˆ์Šต๋‹ˆ๋‹ค.
00:38
And like the rest of the brain, it's made of neurons.
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๋‹ค๋ฅธ ๋‡Œ์กฐ์ง ์ฒ˜๋Ÿผ ๋‰ด๋Ÿฐ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ์Šต๋‹ˆ๋‹ค.
00:40
So the human brain
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์ธ๊ฐ„์˜ ๋‡Œ์—๋Š”
00:42
has about a hundred billion neurons in it.
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๋Œ€๋žต ์ˆ˜์‹ญ์–ต๊ฐœ์˜ ๋‰ด๋Ÿฐ์ด ์žˆ์Šต๋‹ˆ๋‹ค.
00:44
And the neurons communicate with each other
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๋‰ด๋Ÿฐ๋“ค์€ ๊ฐ์ž์˜ ์—ฐ๊ฒฐ์ ์„ ํ†ตํ•ด
00:47
by sending little pulses or spikes of electricity
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๋ฏธ์„ธํ•œ ์ „๊ธฐ์ž๊ทน์„ ๋ณด๋‚ด๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ
00:49
via connections to each other.
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๊ฐ๊ฐ์ด ์ƒํ˜ธ ์ž‘์šฉ์„ ํ•ฉ๋‹ˆ๋‹ค.
00:51
The hippocampus is formed of two sheets of cells,
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์ด ํ•ด๋งˆ๋Š” ์•„์ฃผ ๋ฐ€์ ‘ํ•˜๊ฒŒ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ๋Š”
00:54
which are very densely interconnected.
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๋‘์žฅ์˜ ์„ธํฌ๋“ค๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ์Šต๋‹ˆ๋‹ค.
00:56
And scientists have begun to understand
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๊ณผํ•™์ž๋“ค์€ ๋จน์ด๋ฅผ ์ฐพ์•„์„œ
00:58
how spatial memory works
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๋Œ์•„๋‹ค๋‹ˆ๊ฑฐ๋‚˜ ํ—ค๋งค๋Š”
01:00
by recording from individual neurons
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์ฅ๋“ค์˜ ๋‡Œ์† ๋‰ด๋Ÿฐ๋“ค์„
01:02
in rats or mice
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๊ธฐ๋กํ•จ์œผ๋กœ์จ ๊ณต๊ฐ„๊ธฐ์–ต์ด
01:04
while they forage or explore an environment
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์–ด๋–ป๊ฒŒ ์ž‘๋™ํ•˜๋Š”์ง€
01:06
looking for food.
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์•Œ๊ฒŒ๋˜์—ˆ์Šต๋‹ˆ๋‹ค.
01:08
So we're going to imagine we're recording from a single neuron
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์—ฌ๊ธฐ ์ƒ์ฅ์˜ ํ•ด๋งˆ์† ๋‰ด๋Ÿฐ ํ•œ๊ฐœ๋ฅผ
01:11
in the hippocampus of this rat here.
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๊ณ„์† ๊ธฐ๋กํ•œ๋‹ค๊ณ  ์ƒ๊ฐํ•ด๋ณด์ฃ .
01:14
And when it fires a little spike of electricity,
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๊ทธ ๋‰ด๋Ÿฐ์ด ๋ฏธ์„ธํ•œ ์ „๊ธฐ์ž๊ทน์„ ์ผ์œผํ‚ฌ ๋•Œ,
01:16
there's going to be a red dot and a click.
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๋นจ๊ฐ„์ ์ด ํ•˜๋‚˜ ์ƒ๊ธฐ๊ณ  ํด๋ฆญ ์†Œ๋ฆฌ๊ฐ€ ๋‚ฉ๋‹ˆ๋‹ค.
01:19
So what we see
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์šฐ๋ฆฌ๊ฐ€ ์•Œ์ˆ˜ ์žˆ๋Š”๊ฒƒ์€
01:21
is that this neuron knows
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์ด ๋‰ด๋Ÿฐ์€ ์ฅ๊ฐ€ ์–ด๋””๋ฅผ ๊ฐ€๋Š”์ง€
01:23
whenever the rat has gone into one particular place in its environment.
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์•Œ๊ณ  ์žˆ๋‹ค๋Š”๊ฑฐ์ฃ .
01:26
And it signals to the rest of the brain
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๊ทธ๋ฆฌ๊ณ  ์ด ๋‰ด๋Ÿฐ์€ ๋ฏธ์„ธํ•œ ์ „๊ธฐ์ž๊ทน์„
01:28
by sending a little electrical spike.
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๋‚˜๋จธ์ง€ ๋‹ค๋ฅธ ๋‡Œ์˜ ๊ธฐ๊ด€์œผ๋กœ ๋ณด๋ƒ…๋‹ˆ๋‹ค.
01:31
So we could show the firing rate of that neuron
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๊ทธ๋ž˜์„œ ์ด ์‹คํ—˜์ฅ์˜ ์œ„์น˜๋ฅผ
01:34
as a function of the animal's location.
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๋‰ด๋Ÿฐ์˜ ์ž‘๋™ ๋ฐœ์ƒ ๋น„์œจ๋กœ ๋ณด์—ฌ ๋“œ๋ฆด ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
01:36
And if we record from lots of different neurons,
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๋‹ค๋ฅธ ๋งŽ์€ ๋‰ด๋Ÿฐ๋“ค์˜ ์ž๊ทน์„ ๊ธฐ๋กํ•ด๋ณด๋ฉด,
01:38
we'll see that different neurons fire
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์ฅ๊ฐ€ ์—ฌ๊ธฐ ๋ณด๋Š”๊ฒƒ ์ฒ˜๋Ÿผ
01:40
when the animal goes in different parts of its environment,
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์‚ฌ๊ฐํ˜• ์ƒ์ž์™€ ๊ฐ™์€ ๋‹ค๋ฅธ ํ™˜๊ฒฝ์˜ ์žฅ์†Œ๋กœ ์˜ฎ๊ธฐ๋ฉด
01:42
like in this square box shown here.
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๋‹ค๋ฅธ ๋งŽ์€ ๋‰ด๋Ÿฐ๋“ค์ด ์ž‘๋™ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
01:44
So together they form a map
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๊ทธ๋ž˜์„œ ๋‰ด๋Ÿฐ๋“ค์ด
01:46
for the rest of the brain,
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ํ•จ๊ป˜ ์ง€๋„๋ฅผ ๋งŒ๋“ค๊ฒŒ ๋˜๊ณ ,
01:48
telling the brain continually,
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๋‡Œ์— ์ง€์†์ ์œผ๋กœ ๋ง์„ํ•˜๊ฒŒ ๋˜์ฃ .
01:50
"Where am I now within my environment?"
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"์ด ํ™˜๊ฒฝ์—์„œ ์ง€๊ธˆ ๋‚ด๊ฐ€ ์–ด๋””์— ์žˆ๋Š”๊ฑฐ์ง€?"
01:52
Place cells are also being recorded in humans.
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์žฅ์†Œ ์„ธํฌ๋“ค๋„ ๋ชธ์•ˆ์—์„œ ๊ธฐ๋ก๋ฉ๋‹ˆ๋‹ค.
01:55
So epilepsy patients sometimes need
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๊ทธ๋ž˜์„œ ๊ฐ„์งˆํ™˜์ž๋“ค์˜ ๋‡Œ ๊ฒ€์‚ฌํ•˜๊ธฐ ์œ„ํ•ด
01:57
the electrical activity in their brain monitoring.
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๊ฐ„ํ˜น ์ „๊ธฐ์ ์ธ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๊ธฐ๋„ ํ•ฉ๋‹ˆ๋‹ค.
02:00
And some of these patients played a video game
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์ด๋Ÿฐ ํ™˜์ž๋“ค์ค‘ ์ผ๋ถ€๋Š” ์ž‘์€ ๋„์‹œ๋ฅผ
02:02
where they drive around a small town.
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์šด์ „ํ•˜๊ณ  ๋Œ์•„๋‹ค๋‹ˆ๋Š” ๋น„๋””์˜ค ๊ฒŒ์ž„์„ ํ•ด๋ณด๊ธฐ๋„ํ•ฉ๋‹ˆ๋‹ค.
02:04
And place cells in their hippocampi would fire, become active,
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ํ™˜์ž๋“ค์˜ ํ•ด๋งˆ์†์— ์žฅ์†Œ ์„ธํฌ๊ฐ€ ์ž‘๋™ํ•˜์—ฌ ํ™œ์„ฑํ™”๋˜๊ณ ,
02:07
start sending electrical impulses
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๊ทธ ๋„์‹œ์˜ ํŠน์ • ์žฅ์†Œ๋ฅผ ์ง€๋‚  ๋•Œ๋งˆ๋‹ค
02:10
whenever they drove through a particular location in that town.
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์ „๊ธฐ์ ์ธ ์ž๊ทน์„ ๋ณด๋‚ด๊ธฐ ์‹œ์ž‘ํ•ฉ๋‹ˆ๋‹ค.
02:13
So how does a place cell know
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๊ทธ๋Ÿผ ์žฅ์†Œ ์„ธํฌ๋Š” ์ฅ๋‚˜ ํ™˜์ž๊ฐ€ ์ž์‹ ์ด ์†ํ•œ ํ™˜๊ฒฝ์—์„œ
02:15
where the rat or person is within its environment?
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์–ด๋””์— ์žˆ๋Š”์ง€๋ฅผ ์–ด๋–ป๊ฒŒ ์ธ์ง€ ํ•˜๋Š” ๊ฒƒ ์ผ๊นŒ์š”?
02:18
Well these two cells here
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์—ฌ๊ธฐ ๋‘๊ฐœ์˜ ์„ธํฌ๊ฐ€
02:20
show us that the boundaries of the environment
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์ด ํ™˜๊ฒฝ์˜ ๊ฒฝ๊ณ„์„ ์ด ํŠนํžˆ ์ค‘์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„
02:22
are particularly important.
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๋ณด์—ฌ์ฃผ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
02:24
So the one on the top
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์œ„์ชฝ์— ์žˆ๋Š” ์ด๋ถ€๋ถ„์€
02:26
likes to fire sort of midway between the walls
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์ฅ๊ฐ€ ์žˆ๋˜ ์ƒ์ž์˜ ๋ฒฝ ์‚ฌ์ด ์ค‘๊ฐ„์ง€์ ์—์„œ
02:28
of the box that their rat's in.
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์ž‘๋™ํ•ฉ๋‹ˆ๋‹ค.
02:30
And when you expand the box, the firing location expands.
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์ƒ์ž๋ฅผ ๋Š˜๋ฆฌ๋ฉด, ์ž‘๋™ ์ง€์ ๋„ ๋Š˜์–ด๋‚ฉ๋‹ˆ๋‹ค.
02:33
The one below likes to fire
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๋ฐ‘์— ์žˆ๋Š”๊ฒƒ์€ ๋‚จ์ชฝ ์•„๋ž˜ ๊ฐ€๊นŒ์šด
02:35
whenever there's a wall close by to the south.
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๋ฒฝ์œผ๋กœ ์›€์ง์ผ ๋•Œ๋งˆ๋‹ค ์ž‘๋™ํ•ฉ๋‹ˆ๋‹ค.
02:38
And if you put another wall inside the box,
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์ด ์ƒ์ž์•ˆ์— ๋‹ค๋ฅธ ๋ฒฝ์„ ํ•˜๋‚˜ ์„ธ์šฐ๋ฉด,
02:40
then the cell fires in both place
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์ฅ๊ฐ€ ์ด ์ƒ์ž์•ˆ์„ ๋Œ์•„๋‹ค๋‹ˆ๋‹ค๊ฐ€
02:42
wherever there's a wall to the south
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๋‚จ์ชฝ ์•„๋ž˜ ๋ฒฝ์— ๊ฐˆ๋•Œ๋งˆ๋‹ค
02:44
as the animal explores around in its box.
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์„ธํฌ๊ฐ€ ์ž‘๋™ํ•˜๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
02:48
So this predicts
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์ด ํ˜„์ƒ์€
02:50
that sensing the distances and directions of boundaries around you --
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๊ฑฐ๋ฆฌ์™€ ์ฃผ๋ณ€ ๊ฒฝ๊ณ„์„ ์˜ ๋ฐฉํ–ฅ์€
02:52
extended buildings and so on --
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-- ํ™•์žฅ๋œ ๊ฑด๋ฌผ ๋“ฑ์—์„œ -
02:54
is particularly important for the hippocampus.
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ํ•ด๋งˆ์—๊ฒŒ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋งํ•ฉ๋‹ˆ๋‹ค.
02:57
And indeed, on the inputs to the hippocampus,
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์‹ค์ œ๋กœ, ํ•ด๋งˆ์— ๋“ค์–ด๊ฐ€๋Š” ์ž…๋ ฅ์ •๋ณด๋“ค ์ค‘์—์„œ,
02:59
cells are found which project into the hippocampus,
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์ฅ๊ฐ€ ์—ฌ๊ธฐ์ €๊ธฐ ๋Œ์•„๋‹ค๋‹๋•Œ
03:01
which do respond exactly
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์–ด๋–ค ๋ถ€๋ถ„์ด ๋‹ด๋‹นํ•˜๊ณ 
03:03
to detecting boundaries or edges
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์–ด๋–ค ๋ถ€๋ถ„์ด ๊ฑฐ๋ฆฌ๋‚˜ ๋ฐฉํ–ฅ์— ๋Œ€ํ•ด
03:06
at particular distances and directions
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๊ฒฝ๊ณ„์„ ๊ณผ ๊ฐ€์žฅ์ž๋ฆฌ๋ฅผ
03:08
from the rat or mouse
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ํƒ์ง€ํ•˜๊ฒŒ๋˜๋Š”์ง€์˜ ์ •๋ณด๊ฐ€
03:10
as it's exploring around.
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๋ฐœ๊ฒฌ๋˜์—ˆ์Šต๋‹ˆ๋‹ค.
03:12
So the cell on the left, you can see,
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์™ผ์ชฝ์˜ ์„ธํฌ์—์„œ ๋ณผ ์ˆ˜ ์žˆ๋“ฏ์ด,
03:14
it fires whenever the animal gets near
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๋™์ชฝ์˜ ๋ฒฝ์ด๋‚˜ ๊ฒฝ๊ณ„์„ ์—
03:16
to a wall or a boundary to the east,
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๊ฐ€๊นŒ์ด ๊ฐˆ๋•Œ๋งˆ๋‹ค,
03:19
whether it's the edge or the wall of a square box
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์ด๊ฒƒ์ด ์ƒ์ž์˜ ๊ฒฝ๊ณ„๋‚˜ ๋ฒฝ์ธ์ง€,
03:22
or the circular wall of the circular box
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๋˜๋Š” ๋‘ฅ๊ทผ ์ƒ์ž์˜ ๋ฒฝ์ด๋‚˜ ์ฅ๊ฐ€ ๋Œ์•„๋‹ค๋‹ˆ๋˜
03:24
or even the drop at the edge of a table, which the animals are running around.
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ํ…Œ์ด๋ธ”์˜ ๋์ž๋ฝ์ธ์ง€ ํƒ์ง€ํ•˜๋ ค๊ณ  ์„ธํฌ๊ฐ€ ์ž‘๋™ํ•ฉ๋‹ˆ๋‹ค.
03:27
And the cell on the right there
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์˜ค๋ฅธ์ชฝ์˜ ์„ธํฌ๋Š”
03:29
fires whenever there's a boundary to the south,
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๋‚จ์ชฝ ๊ฒฝ๊ณ„๋กœ ๊ฐˆ๋•Œ๋งˆ๋‹ค ์ž‘๋™ํ•˜๋Š”๋ฐ์š”,
03:31
whether it's the drop at the edge of the table or a wall
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์ด๋ถ€๋ถ„์ด ํ…Œ์ด๋ธ”์ด๋‚˜ ๋ฒฝ์˜ ๋์ธ์ง€,
03:33
or even the gap between two tables that are pulled apart.
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๋Œ์–ด๋‹น๊ฒจ๋†“์€ ํ…Œ์ด๋ธ” ์‚ฌ์ด์˜ ๊ฐ„๊ฒฉ์ธ์ง€๋ฅผ ํƒ์ง€ํ•ฉ๋‹ˆ๋‹ค.
03:36
So that's one way in which we think
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์ด๋Ÿฐ๊ฒƒ๋“ค์ด ์žฅ์†Œ ์„ธํฌ๊ฐ€ ์ฅ๊ฐ€ ๋Œ์•„๋‹ค๋‹๋•Œ
03:38
place cells determine where the animal is as it's exploring around.
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์ž์‹ ์ด ์–ด๋””์— ์žˆ๋Š”์ง€๋ฅผ ์ธ์ง€ํ•˜๋Š” ํ•˜๋‚˜์˜ ๋ฐฉ๋ฒ•์ž…๋‹ˆ๋‹ค.
03:41
We can also test where we think objects are,
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์•„์ฃผ ๋‹จ์ˆœํ•œ ํ™˜๊ฒฝ์—์„œ ์ด ๊นƒ๋ฐœ์ฒ˜๋Ÿผ ์‚ฌ๋ฌผ๋“ค์ด
03:44
like this goal flag, in simple environments --
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์–ด๋””์— ์žˆ๋Š”์ง€๋ฅผ ์ธ์ง€ํ•˜๋Š” ์‹คํ—˜๋„ ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
03:47
or indeed, where your car would be.
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-- ์•„๋‹ˆ๋ฉด ์—ฌ๋Ÿฌ๋ถ„์˜ ์ž๋™์ฐจ๊ฐ€ ๋  ์ˆ˜๋„ ์žˆ๊ตฌ์š” --
03:49
So we can have people explore an environment
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์ด๋Ÿฐ ๊ณต๊ฐ„์„ ๋Œ์•„๋‹ค๋‹ˆ๋Š” ์‚ฌ๋žŒ์ด ์žˆ๋‹ค๊ณ  ํ•˜๊ณ ,
03:52
and see the location they have to remember.
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์ด ์‚ฌ๋žŒ์ด ๊ธฐ์–ตํ•ด์•ผํ•˜๋Š” ์œ„์น˜๋ฅผ ๋ด…๋‹ˆ๋‹ค.
03:55
And then, if we put them back in the environment,
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๊ทธ๋ฆฌ๊ณ ๋‚˜์„œ ์‚ฌ๋žŒ์„ ๋‹ค์‹œ ์ด ํ™˜๊ฒฝ์— ๊ฐ€์ ธ๋‹ค ๋†“์œผ๋ฉด,
03:57
generally they're quite good at putting a marker down
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๊นƒ๋ฐœ์ด๋‚˜ ์ž์‹ ์˜ ์ž๋™์ฐจ๊ฐ€ ์žˆ์—ˆ๋‹ค๊ณ  ์ƒ๊ฐํ•˜๋Š”
03:59
where they thought that flag or their car was.
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์žฅ์†Œ์— ์ผ๋ฐ˜์ ์œผ๋กœ ํ‘œ์‹œ๋ฅผ ์ž˜ ํ•˜๊ฒŒ๋ฉ๋‹ˆ๋‹ค.
04:02
But on some trials,
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ํ•˜์ง€๋งŒ ๋‹ค๋ฅธ ์‹คํ—˜์—์„œ๋Š”,
04:04
we could change the shape and size of the environment
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์žฅ์†Œ ์„ธํฌ๋ฅผ ๊ฐ€์ง€๊ณ  ํ–ˆ๋˜ ๊ฒƒ ์ฒ˜๋Ÿผ
04:06
like we did with the place cell.
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๊ณต๊ฐ„์˜ ํ˜•ํƒœ๋‚˜ ํฌ๊ธฐ๋ฅผ ๋ฐ”๊พธ์–ด ๋ณด์•˜์Šต๋‹ˆ๋‹ค.
04:08
In that case, we can see
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์ด ๊ฒฝ์šฐ์—,
04:10
how where they think the flag had been changes
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์‚ฌ๋žŒ๋“ค์ด ๊นƒ๋ฐœ์˜ ์œ„์น˜๊ฐ€ ์–ด๋””๋กœ
04:13
as a function of how you change the shape and size of the environment.
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๋ฐ”๋€Œ์—ˆ๋Š”์ง€๋ฅผ ์–ด๋–ป๊ฒŒ ์ƒ๊ฐํ•˜๋Š”์ง€ ์•Œ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
04:16
And what you see, for example,
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์˜ˆ๋ฅผ๋“ค์–ด,
04:18
if the flag was where that cross was in a small square environment,
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๊นƒ๋ฐœ์ด ์ž‘์€ ์‚ฌ๊ฐํ˜•์•ˆ์— ์—‘์Šคํ‘œ๊ฐ€ ์žˆ๋Š”๊ณณ์— ์žˆ๊ณ ,
04:21
and then if you ask people where it was,
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๊นƒ๋ฐœ์ด ์–ด๋””์— ์žˆ์—ˆ๋Š”์ง€ ๋ฌผ์–ด๋ณธ๋‹ค๋ฉด,
04:23
but you've made the environment bigger,
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ํ•˜์ง€๋งŒ ์ด ์žฅ์†Œ๋ฅผ ๋” ํฌ๊ฒŒ ํ™•๋Œ€ํ–ˆ์ฃ ,
04:25
where they think the flag had been
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์‚ฌ๋žŒ๋“ค์€ ๊นƒ๋ฐœ์ด
04:27
stretches out in exactly the same way
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์žฅ์†Œ ์„ธํฌ๊ฐ€ ๋Š˜๋ฆฐ๊ฒƒ๊ณผ ๊ฐ™์€ ๋ฐฉ๋ฒ•์œผ๋กœ
04:29
that the place cell firing stretched out.
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๊นƒ๋ฐœ์ด ๋Š˜์–ด๋‚œ๊ณณ์— ์žˆ๋‹ค๊ณ  ์ƒ๊ฐํ•ฉ๋‹ˆ๋‹ค.
04:31
It's as if you remember where the flag was
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์ด๊ฑด ๋งˆ์น˜ ๊ทธ ์œ„์น˜์—์„œ
04:33
by storing the pattern of firing across all of your place cells
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์žฅ์†Œ ์„ธํฌ๊ฐ€ ์ž‘๋™ํ•˜๋Š” ๋ชจ๋“  ํŒจํ„ด์„ ์ €์žฅํ•˜์—ฌ
04:36
at that location,
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๊นƒ๋ฐœ์˜ ์œ„์น˜๋ฅผ ๊ธฐ์–ตํ•˜๋Š” ๊ฒƒ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค.
04:38
and then you can get back to that location
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๊ทธ๋ฆฌ๊ณ  ๋Œ์•„๋‹ค๋‹ˆ๋‹ค๊ฐ€
04:40
by moving around
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๊ทธ ์ง€์ ์œผ๋กœ ๋‹ค์‹œ ๋ณต๊ท€ํ•˜๋Š”๋ฐ,
04:42
so that you best match the current pattern of firing of your place cells
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์ด๋Š” ์ด๋ฏธ ์ €์žฅ๋œ ํŒจํ„ด๊ณผ ์žฅ์†Œ ์„ธํฌ๊ฐ€ ์ž‘๋™ํ•˜๋Š” ํ˜„์žฌ์˜
04:44
with that stored pattern.
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ํŒจํ„ด์„ ๋งค์นญํ•˜๊ธฐ ๋•Œ๋ฌธ์ด์ฃ .
04:46
That guides you back to the location that you want to remember.
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์ด๋Ÿฐ ๋ฐฉ๋ฒ•์ด ๊ธฐ์–ตํ•˜๊ณ  ์‹ถ์€ ์œ„์น˜๋กœ ๋Œ์•„ ๊ฐˆ ์ˆ˜ ์žˆ๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค.
04:49
But we also know where we are through movement.
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ํ•˜์ง€๋งŒ ์›€์ง์ž„์„ ํ†ตํ•ด์„œ๋„ ์šฐ๋ฆฌ๊ฐ€ ์–ด๋””์— ์žˆ๋Š”์ง€๋ฅผ ์•Œ๊ฒŒ๋ฉ๋‹ˆ๋‹ค.
04:52
So if we take some outbound path --
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์™ธ๋ถ€์— ๋‚˜๊ฐ€๋Š” ๊ฒฝ๋กœ๋Š” ์ƒ๊ฐํ•ด๋ณด๋ฉด,
04:54
perhaps we park and we wander off --
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-- ์•„๋งˆ๋„ ์ฃผ์ฐจ๋ฅผ ํ•˜๊ฑฐ๋‚˜ ๊ทธ๋ƒฅ ๋ฐฐํšŒํ•˜๋Š” ๊ฒฝ์šฐ์ฃ  --
04:56
we know because our own movements,
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์šฐ๋ฆฌ๊ฐ€ ์›€์ง์ด๋Š” ๊ฒฝ๋กœ์ด๊ธฐ ๋•Œ๋ฌธ์—,
04:58
which we can integrate over this path
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์ง„ํ–‰ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋Œ์•„๋„๋ก
05:00
roughly what the heading direction is to go back.
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๋Œ€๋žต ํ•ฉ์น  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
05:02
And place cells also get this kind of path integration input
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๊ทธ๋ฆฌ๊ณ  ์žฅ์†Œ ์„ธํฌ๋“ค์€ ๊ฒฉ์ž ์„ธํฌ๋ผ๊ณ 
05:06
from a kind of cell called a grid cell.
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๋ถˆ๋ฆฌ๋Š” ์„ธํฌ๋“ค๋กœ๋ถ€ํ„ฐ ๊ฒฝ๋กœ์— ๋Œ€ํ•œ ํ†ตํ•ฉ ์ž…๋ ฅ ์ •๋ณด๋ฅผ ์–ป์Šต๋‹ˆ๋‹ค.
05:09
Now grid cells are found, again,
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๊ฒฉ์ž ์„ธํฌ๋Š” ํ•ด๋งˆ์˜
05:11
on the inputs to the hippocampus,
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์ž…๋ ฅ ์ •๋ณด์— ์žˆ๊ตฌ์š”,
05:13
and they're a bit like place cells.
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์žฅ์†Œ ์„ธํฌ์™€ ์•ฝ๊ฐ„ ๋น„์Šทํ•ฉ๋‹ˆ๋‹ค.
05:15
But now as the rat explores around,
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ํ•˜์ง€๋งŒ ์ฅ๊ฐ€ ์—ฌ๊ธฐ์ €๊ธฐ ๋Œ์•„๋‹ค๋‹ˆ๋ฉด์„œ,
05:17
each individual cell fires
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๊ฐ๊ฐ์˜ ๊ฐœ๋ณ„ ์„ธํฌ๊ฐ€
05:19
in a whole array of different locations
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๋†€๋ž๊ฒŒ๋„ ์œ ์‚ฌํ•œ ์‚ผ๊ฐ ๊ฒฉ์žํ˜•ํƒœ์˜
05:22
which are laid out across the environment
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๋ฐฐ์—ด ๋ชจ์–‘์„ ๊ฐ€์ง€๊ณ 
05:24
in an amazingly regular triangular grid.
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์ž‘๋™ํ•˜๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
05:29
And if you record from several grid cells --
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๋ช‡๊ฐœ์˜ ๊ฒฉ์ž ์„ธํฌ๋“ค์„ ๊ธฐ๋กํ•ด๋ณด๋ฉด,
05:32
shown here in different colors --
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-- ๋‹ค๋ฅธ ์ƒ‰๊น”๋กœ ๋ณด์ด๊ฒŒ ํ•ด๋ณด์ฃ  --
05:34
each one has a grid-like firing pattern across the environment,
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๊ฐ ์„ธํฌ๋“ค์€ ์ด ํ™˜๊ฒฝ์•ˆ์—์„œ ๊ฒฉ์ž ๋ชจ์–‘์˜ ์ž‘๋™ ํŒจํ„ด์„ ๊ฐ€์ง‘๋‹ˆ๋‹ค.
05:37
and each cell's grid-like firing pattern is shifted slightly
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๊ทธ๋ฆฌ๊ณ  ๊ฐ ์„ธํฌ์˜ ๊ฒฉ์ž ๋ชจ์–‘ ์ž‘๋™ ํŒจํ„ด์€ ๋‹ค๋ฅธ ์„ธํฌ๋“ค์— ๋น„ํ•ด
05:40
relative to the other cells.
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์กฐ๊ธˆ ์ด๋™ํ•˜๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
05:42
So the red one fires on this grid
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๊ทธ๋ž˜์„œ ์ด ๊ฒฉ์ž์—์„œ๋Š” ๋นจ๊ฐ„์ƒ‰์ด ์ž‘๋™ํ•˜๊ณ ,
05:44
and the green one on this one and the blue on on this one.
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์—ฌ๊ธฐ์„œ๋Š” ๋…น์ƒ‰์ด, ๊ทธ๋ฆฌ๊ณ  ์ด๊ณณ์—์„œ๋Š” ํŒŒ๋ž€์ƒ‰์ž…๋‹ˆ๋‹ค.
05:47
So together, it's as if the rat
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์ด๊ฒƒ์€ ๋งˆ์น˜ ์ฅ๊ฐ€
05:50
can put a virtual grid of firing locations
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๊ฐ€์ƒ์˜ ์ž‘๋™ ์œ„์น˜์— ๋Œ€ํ•œ ๊ฒฉ์ž๋ฅผ
05:52
across its environment --
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์ด ํ™˜๊ฒฝ์— ๊ทธ๋ฆฐ ๊ฒƒ ๊ฐ™์Šต๋‹ˆ๋‹ค.
05:54
a bit like the latitude and longitude lines that you'd find on a map,
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-- ์ง€๋„์ƒ์˜ ์œ„๋„์™€ ๊ฒฝ๋„์™€ ์กฐ๊ธˆ ์œ ์‚ฌํ•˜์ฃ . --
05:57
but using triangles.
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์‚ผ๊ฐํ˜•์„ ์‚ฌ์šฉํ•œ๊ฒƒ๋งŒ ๋นผ๊ตฌ์š”.
05:59
And as it moves around,
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์ฅ๊ฐ€ ์›€์ง์ด๋ฉด์„œ,
06:01
the electrical activity can pass
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์ „๊ธฐ์ ์ธ ์ž๊ทน์ด ์ฅ๊ฐ€ ์–ด๋””์— ์žˆ๋Š”์ง€
06:03
from one of these cells to the next cell
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ํ•œ ์„ธํฌ์—์„œ ๋‹ค๋ฅธ ์„ธํฌ๋กœ
06:05
to keep track of where it is,
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์ „๋‹ฌํ•˜๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
06:07
so that it can use its own movements
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๋”ฐ๋ผ์„œ ์ฅ๋“ค์€ ์ž์‹ ์˜ ์›€์ง์ž„ ๋•Œ๋ฌธ์—
06:09
to know where it is in its environment.
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์ด ํ™˜๊ฒฝ์—์„œ ์–ด๋””์— ์œ„์น˜ํ•˜๋Š”์ง€๋ฅผ ์•Œ๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
06:11
Do people have grid cells?
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์‚ฌ๋žŒ์ด ๊ฒฉ์ž ์„ธํฌ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์„ ๊นŒ์š”?
06:13
Well because all of the grid-like firing patterns
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๊ฒฉ์ž ๋ชจ์–‘ ์ž‘๋™ ํŒจํ„ด๋“ค์€
06:15
have the same axes of symmetry,
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๊ฐ™์€ ๋Œ€์นญ์ถ•๊ณผ ์˜ค๋ Œ์ง€ ์ƒ‰์œผ๋กœ ๋ณด์ด๋Š”
06:17
the same orientations of grid, shown in orange here,
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๊ฐ™์€ ๊ฒฉ์ž ๋ฐฉํ–ฅ์„ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
06:20
it means that the net activity
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๋‡Œ์˜ ํŠน๋ณ„ํ•œ ๋ถ€๋ถ„์— ์žˆ๋Š” ๊ฒฉ์ž์„ธํฌ์˜
06:22
of all of the grid cells in a particular part of the brain
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์ด๋Ÿฐ ํ–‰๋™๋“ค์€ ์ด๋ ‡๊ฒŒ 6๊ฐœ์˜
06:25
should change
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๋ฐฉํ–ฅ์œผ๋กœ ์›€์ง์ด๋Š”์ง€,
06:27
according to whether we're running along these six directions
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์•„๋‹ˆ๋ฉด 6๊ฐœ์˜ ๋ฐฉํ–ฅ์ค‘ ํ•œ ๋ฐฉํ–ฅ์œผ๋กœ๋งŒ ์›€์ง์ด๋Š”์ง€์—
06:29
or running along one of the six directions in between.
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๋”ฐ๋ผ์„œ ๋ฐ”๊ฟ”์•ผ๋งŒ ํ•œ๋‹ค๋Š”๊ฒƒ์„ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค.
06:32
So we can put people in an MRI scanner
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๊ทธ๋ž˜์„œ ์‚ฌ๋žŒ์„ MRI ๊ฒ€์‚ฌ๊ธฐ์— ์˜ฌ๋ ค๋†“๊ณ 
06:34
and have them do a little video game
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์ œ๊ฐ€ ๋ณด์—ฌ๋“œ๋ ธ๋˜ ๊ทธ๋Ÿฐ ๋น„๋””์˜ค ๊ฒŒ์ž„์„
06:36
like the one I showed you
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ํ•˜๊ฒŒํ•˜๊ณ , ์ด ์‹ ํ˜ธ๋ฅผ
06:38
and look for this signal.
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์ฐพ์•„๋ด…๋‹ˆ๋‹ค.
06:40
And indeed, you do see it in the human entorhinal cortex,
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์ด ์„ธํฌ๋Š” ์‚ฌ๋žŒ์˜ ๋Œ€๋‡Œํ”ผ์งˆ์—์„œ๋„ ๋ณผ ์ˆ˜ ์žˆ๋Š”๋ฐ์š”,
06:43
which is the same part of the brain that you see grid cells in rats.
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์ฅ์˜ ๋‡Œ์—์„œ ๋ดค๋˜ ๊ฒฉ์ž ์„ธํฌ์™€ ๊ฐ™์€ ๋ถ€๋ถ„์ž…๋‹ˆ๋‹ค.
06:46
So back to Homer.
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๊ทธ๋Ÿผ ํ˜ธ๋จธ ์ด์•ผ๊ธฐ๋กœ ๋Œ์•„๊ฐ€์„œ์š”.
06:48
He's probably remembering where his car was
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ํ˜ธ๋จธ๋Š” ์–ด๋””์— ์ฃผ์ฐจ๋ฅผ ํ–ˆ์—ˆ๋Š”์ง€๋ฅผ
06:50
in terms of the distances and directions
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์ฃผ์ฐจํ–ˆ๋˜ ์œ„์น˜ ์ฃผ๋ณ€์˜ ํ™•์žฅ๋œ ๊ฑด๋ฌผ๊ณผ
06:52
to extended buildings and boundaries
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๊ฒฝ๊ณ„์„ ์˜ ๊ฑฐ๋ฆฌ์™€ ๋ฐฉํ–ฅ์„ ๊ฐ€์ง€๊ณ 
06:54
around the location where he parked.
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๊ธฐ์–ตํ•˜๊ณ  ์žˆ์„ ๊ฒ๋‹ˆ๋‹ค.
06:56
And that would be represented
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๊ทธ ๊ธฐ์–ต์€ ๊ฒฝ๊ณ„ ํƒ์ง€ ์„ธํฌ๊ฐ€
06:58
by the firing of boundary-detecting cells.
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์ž‘๋™ํ•˜์—ฌ ๊ทธ๋ ค์งˆ ๊ฒƒ์ž…๋‹ˆ๋‹ค.
07:00
He's also remembering the path he took out of the car park,
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๋˜ํ•œ ๊ฒฉ์ž ์„ธํฌ๋“ค์ด ์ž‘๋™ํ•˜์—ฌ
07:03
which would be represented in the firing of grid cells.
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์ฃผ์ฐจ์žฅ์—์„œ ๋น ์ ธ๋‚˜๊ฐ€๋Š” ๊ฒฝ๋กœ๋ฅผ ๊ธฐ์–ตํ•  ๊ฒƒ์ž…๋‹ˆ๋‹ค.
07:06
Now both of these kinds of cells
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์ด๋Ÿฐ ๋‘์ข…๋ฅ˜์˜ ์„ธํฌ๋“ค์ด
07:08
can make the place cells fire.
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์žฅ์†Œ ์„ธํฌ๋“ค์ด ์ž‘๋™ํ•˜๋„๋ก ๋งŒ๋“ค์ฃ .
07:10
And he can return to the location where he parked
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๊ทธ๋ฆฌ๊ณ  ํ˜ธ๋จธ๋Š” ์ด์ „์— ์ฃผ์ฐจํ–ˆ๋˜
07:12
by moving so as to find where it is
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์ €์žฅ๋œ ํŒจํ„ด๊ณผ ํ˜„์žฌ ๋จธ๋ฆฌ ์†์—์„œ
07:15
that best matches the firing pattern
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์ž‘๋™ํ•˜๋Š” ์žฅ์†Œ ์„ธํฌ๋“ค์˜ ํŒจํ„ด์ค‘
07:17
of the place cells in his brain currently
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๊ฐ€์žฅ ์ž˜ ๋งž๋Š” ํŒจํ„ด์„ ์ฐพ์•„์„œ
07:19
with the stored pattern where he parked his car.
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์ฃผ์ฐจํ•œ ์œ„์น˜๋กœ ๋Œ์•„ ๊ฐˆ ์ˆ˜ ์žˆ๊ฒŒ ๋˜๋Š”๊ฒƒ์ด์ฃ .
07:22
And that guides him back to that location
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์ด๋Ÿฐ ๋ฐฉ์‹์ด
07:24
irrespective of visual cues
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์‹œ๊ฐ์ ์ธ ์‹ ํ˜ธ์— ๊ด€๊ณ„์—†์ด
07:26
like whether his car's actually there.
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ํ˜ธ๋จธ๋ฅผ ์ฃผ์ฐจ์œ„์น˜๋กœ ๋Œ์•„๊ฐ€๊ฒŒ ๋„์™€ ์ฃผ๋Š”๊ฒƒ์ด์ฃ .
07:28
Maybe it's been towed.
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์•„๋งˆ ๊ทธ๋ƒฅ ๋Œ๊ณ  ๋‚˜์˜ฌ ์ˆ˜ ๋„ ์žˆ๊ฒ ์ฃ .
07:30
But he knows where it was, so he knows to go and get it.
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ํ•˜์ง€๋งŒ ํ˜ธ๋จธ๋Š” ์–ด๋””์— ์ฐจ๊ฐ€ ์žˆ๋Š”์ง€ ์•Œ๊ณ  ๊ฐ€์„œ ๊ฐ€์ ธ์˜ต๋‹ˆ๋‹ค.
07:33
So beyond spatial memory,
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๊ณต๊ฐ„ ๊ธฐ์–ต์„ ๋„˜์–ด์„œ,
07:35
if we look for this grid-like firing pattern
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์ „์ฒด ๋‡Œ๋ฅผ ํ†ตํ•ด ๊ฒฉ์ž ๋ชจ์–‘์˜
07:37
throughout the whole brain,
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์ž‘๋™ ํŒจํ„ด์„ ์ฐพ์•„๋ณด๋ฉด,
07:39
we see it in a whole series of locations
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์˜ˆ๋ฅผ๋“ค์–ด, ์ง€๋‚œ๋ฒˆ์— ๊ฐ”์—ˆ๋˜ ์˜ˆ์‹์žฅ์„
07:42
which are always active
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๊ธฐ์–ตํ•˜๋Š”๊ฒƒ ์ฒ˜๋Ÿผ ์ž์‹  ๊ธฐ์–ต์†์— ์žˆ๋Š”
07:44
when we do all kinds of autobiographical memory tasks,
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๋ชจ๋“  ์ผ๋“ค์„ ๊ธฐ์–ตํ•ด ๋‚ผ๋•Œ ํ™œ์„ฑํ™”๋˜๋Š”
07:46
like remembering the last time you went to a wedding, for example.
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์ผ๋ จ์˜ ์œ„์น˜๋“ค์—์„œ ์ฐพ์•„ ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
07:49
So it may be that the neural mechanisms
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์šฐ๋ฆฌ ์ฃผ๋ณ€์˜ ๊ณต๊ฐ„์„ ํ‘œ์‹œํ•˜๋Š”
07:51
for representing the space around us
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์‹ ๊ฒฝ ๋ฉ”์ปค๋‹ˆ์ฆ˜์€
07:54
are also used for generating visual imagery
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์‹œ๊ฐ์ ์ธ ํ˜•์ƒ์„ ์ƒ์„ฑํ•˜๋Š”๋ฐ ์‚ฌ์šฉ๋˜๋Š”๋ฐ์š”,
07:58
so that we can recreate the spatial scene, at least,
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๊ทธ๋ž˜์„œ ์šฐ๋ฆฌ๋Š” ์ƒ์ƒํ•  ๋•Œ ์šฐ๋ฆฌ์—๊ฒŒ ๋ฐœ์ƒํ•˜๋Š”
08:01
of the events that have happened to us when we want to imagine them.
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๊ณต๊ฐ„ ์žฅ๋ฉด์„ ๋‹ค์‹œ ๋งŒ๋“ค์–ด ๋‚ผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
08:04
So if this was happening,
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๊ทธ๋ž˜์„œ ์ด๋Ÿฐ ํ˜„์ƒ์ด ์ผ์–ด๋‚˜๋ฉด,
08:06
your memories could start by place cells activating each other
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์—ฌ๋Ÿฌ๋ถ„์˜ ๊ธฐ์–ต๋“ค์€ ๋ฐ€์ ‘ํ•˜๊ฒŒ ์—ฐ๊ฒฐ๋œ
08:09
via these dense interconnections
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์žฅ์†Œ ์„ธํฌ๋“ค์ด ์„œ๋กœ ํ™œ์„ฑํ™”ํ•ด์ฃผ์–ด ๊ธฐ์–ต์ด ์‹œ์ž‘๋˜๊ณ 
08:11
and then reactivating boundary cells
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์—ฌ๋Ÿฌ๋ถ„์˜ ์‹œ์•ผ์— ๋“ค์–ด์˜ค๋Š” ์žฅ๋ฉด๋“ค์˜
08:13
to create the spatial structure
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๊ณต๊ฐ„๊ตฌ์กฐ๋ฅผ ๋งŒ๋“ค๊ธฐ ์œ„ํ•ด
08:15
of the scene around your viewpoint.
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๊ฒฝ๊ณ„ ์„ธํฌ๋“ค์„ ์žฌ ํ™œ์„ฑํ™” ํ•˜๋Š”๊ฒƒ ์ž…๋‹ˆ๋‹ค.
08:17
And grid cells could move this viewpoint through that space.
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๊ทธ๋ฆฌ๊ณ  ๊ฒฉ์ž ์„ธํฌ๋“ค์ด ๊ณต๊ฐ„์„ ํ†ตํ•ด์„œ ์‹œ์•ผ๋ฅผ ์›€์ง์ด์ฃ .
08:19
Another kind of cell, head direction cells,
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์•„์ง ์–ธ๊ธ‰ํ•˜์ง€ ์•Š๋Š” ๋˜๋‹ค๋ฅธ ์„ธํฌ์ธ,
08:21
which I didn't mention yet,
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์ง์ง„ ๋ฐฉํ–ฅ ์„ธํฌ๋Š”
08:23
they fire like a compass according to which way you're facing.
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์‚ฌ๋žŒ์ด ๋งˆ์ฃผ์น˜๊ฒŒ ๋˜๋Š” ๋ฐฉ๋ฒ•์— ์˜ํ•ด ๋‚˜์นจ๋ฐ˜ ์ฒ˜๋Ÿผ ์ž‘๋™ํ•ฉ๋‹ˆ๋‹ค.
08:26
They could define the viewing direction
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์ด ์„ธํฌ๋“ค์€ ์‹œ๊ฐ์ ์ธ ํ˜•์ƒํ™”์—์„œ
08:28
from which you want to generate an image for your visual imagery,
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ํ•˜๋‚˜์˜ ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์—์„œ ์‹œ์•ผ์˜ ๋ฐฉํ–ฅ์„ ์ •์˜ํ•ฉ๋‹ˆ๋‹ค.
08:31
so you can imagine what happened when you were at this wedding, for example.
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๊ทธ๋ž˜์„œ ์˜ˆ๋ฅผ๋“ค์–ด, ์ด ๊ฒฐํ˜ผ์‹์„ ์ฐธ์„ํ–ˆ์„ ๋•Œ ๋ฌด์Šจ์ผ์ด ์žˆ์—ˆ๋Š”์ง€ ์ƒ์ƒํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
08:34
So this is just one example
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์ด๊ฒƒ์ด ์šฐ๋ฆฌ์˜ ๋‡Œ๋ฅผ
08:36
of a new era really
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์ด๋ฃจ๊ณ  ์žˆ๋Š” ์ˆ˜์‹ญ์–ต๊ฐœ์˜ ๋‰ด๋Ÿฐ๋“ค์˜ ์ž‘์šฉ์—์„œ
08:38
in cognitive neuroscience
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์‚ฌ๋žŒ์ด ์–ด๋–ป๊ฒŒ ๊ธฐ์–ตํ•˜๋Š”์ง€,
08:40
where we're beginning to understand
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๋˜๋Š” ์ƒ์ƒํ•˜๊ณ , ์‚ฌ๊ณ ํ•˜๋Š”๊ฐ€ ์ฒ˜๋Ÿผ
08:42
psychological processes
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์‹ฌ๋ฆฌ์ ์ธ ๊ณผ์ •์„
08:44
like how you remember or imagine or even think
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์ดํ•ดํ•˜๊ธฐ ์‹œ์ž‘ํ•œ
08:47
in terms of the actions
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์ธ์ง€ ์‹ ๊ฒฝ๊ณผํ•™์˜
08:49
of the billions of individual neurons that make up our brains.
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ํ•œ๊ฐ€์ง€ ์˜ˆ์ž…๋‹ˆ๋‹ค.
08:52
Thank you very much.
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๋Œ€๋‹จํžˆ ๊ฐ์‚ฌํ•ฉ๋‹ˆ๋‹ค.
08:54
(Applause)
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(๋ฐ•์ˆ˜)
์ด ์›น์‚ฌ์ดํŠธ ์ •๋ณด

์ด ์‚ฌ์ดํŠธ๋Š” ์˜์–ด ํ•™์Šต์— ์œ ์šฉํ•œ YouTube ๋™์˜์ƒ์„ ์†Œ๊ฐœํ•ฉ๋‹ˆ๋‹ค. ์ „ ์„ธ๊ณ„ ์ตœ๊ณ ์˜ ์„ ์ƒ๋‹˜๋“ค์ด ๊ฐ€๋ฅด์น˜๋Š” ์˜์–ด ์ˆ˜์—…์„ ๋ณด๊ฒŒ ๋  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ฐ ๋™์˜์ƒ ํŽ˜์ด์ง€์— ํ‘œ์‹œ๋˜๋Š” ์˜์–ด ์ž๋ง‰์„ ๋”๋ธ” ํด๋ฆญํ•˜๋ฉด ๊ทธ๊ณณ์—์„œ ๋™์˜์ƒ์ด ์žฌ์ƒ๋ฉ๋‹ˆ๋‹ค. ๋น„๋””์˜ค ์žฌ์ƒ์— ๋งž์ถฐ ์ž๋ง‰์ด ์Šคํฌ๋กค๋ฉ๋‹ˆ๋‹ค. ์˜๊ฒฌ์ด๋‚˜ ์š”์ฒญ์ด ์žˆ๋Š” ๊ฒฝ์šฐ ์ด ๋ฌธ์˜ ์–‘์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ฌธ์˜ํ•˜์‹ญ์‹œ์˜ค.

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