ABOUT THE SPEAKER
Ed Boyden - Neuroengineer
Ed Boyden is a professor of biological engineering and brain and cognitive sciences at the MIT Media Lab and the MIT McGovern Institute.

Why you should listen

Ed Boyden leads the Synthetic Neurobiology Group, which develops tools for analyzing and repairing complex biological systems such as the brain. His group applies these tools in a systematic way in order to reveal ground truth scientific understandings of biological systems, which in turn reveal radical new approaches for curing diseases and repairing disabilities. These technologies include expansion microscopy, which enables complex biological systems to be imaged with nanoscale precision, and optogenetic tools, which enable the activation and silencing of neural activity with light (TED Talk: A light switch for neurons). Boyden also co-directs the MIT Center for Neurobiological Engineering, which aims to develop new tools to accelerate neuroscience progress.

Amongst other recognitions, Boyden has received the Breakthrough Prize in Life Sciences (2016), the BBVA Foundation Frontiers of Knowledge Award (2015), the Carnegie Prize in Mind and Brain Sciences (2015), the Jacob Heskel Gabbay Award (2013), the Grete Lundbeck Brain Prize (2013) and the NIH Director's Pioneer Award (2013). He was also named to the World Economic Forum Young Scientist list (2013) and the Technology Review World's "Top 35 Innovators under Age 35" list (2006). His group has hosted hundreds of visitors to learn how to use new biotechnologies and spun out several companies to bring inventions out of his lab and into the world. Boyden received his Ph.D. in neurosciences from Stanford University as a Hertz Fellow, where he discovered that the molecular mechanisms used to store a memory are determined by the content to be learned. Before that, he received three degrees in electrical engineering, computer science and physics from MIT. He has contributed to over 300 peer-reviewed papers, current or pending patents and articles, and he has given over 300 invited talks on his group's work.

More profile about the speaker
Ed Boyden | Speaker | TED.com
TED2011

Ed Boyden: A light switch for neurons

Ed Boyden: Un interruptor para neuronas

Filmed:
1,098,379 views

Ed Boyden amósamos como, inserindo xenes con proteínas sensibles á luz en células cerebrais, pode activar ou desactivar selectivamente neuronas específicas con implantes de fibra óptica. Con este nivel de control sen precedentes, é capaz de curar ratos con trastornos similares ao estrés postraumático e certas formas de cegueira. No horizonte: próteses neurais. O presentador da sesión, Juan Enríquez, lidera unha breve charla final de preguntas e respostas.
- Neuroengineer
Ed Boyden is a professor of biological engineering and brain and cognitive sciences at the MIT Media Lab and the MIT McGovern Institute. Full bio

Double-click the English transcript below to play the video.

00:15
Think about your day for a second.
0
0
2000
Pensen no seu día por un segundo.
00:17
You woke up, felt fresh air on your face as you walked out the door,
1
2000
3000
Espertaron, sentiron o aire fresco
na cara ao saír pola porta,
00:20
encountered new colleagues and had great discussions,
2
5000
2000
atoparon novos colegas
e tiveron bos debates,
00:22
and felt in awe when you found something new.
3
7000
2000
e asombráronse ao atopar algo novo.
00:24
But I bet there's something you didn't think about today --
4
9000
2000
Pero aposto a que hoxe non pensaron
00:26
something so close to home
5
11000
2000
en algo tan próximo a nós
00:28
that you probably don't think about it very often at all.
6
13000
2000
que non atrae a nosa atención moi a miúdo.
00:30
And that's that all the sensations, feelings,
7
15000
2000
Que todas esas sensacións, sentimentos,
00:32
decisions and actions
8
17000
2000
decisións e accións
00:34
are mediated by the computer in your head
9
19000
2000
son intermediadas
por unha computadora na súa cabeza
00:36
called the brain.
10
21000
2000
á que chamamos cerebro.
00:38
Now the brain may not look like much from the outside --
11
23000
2000
E pode non parecer gran cousa dende fóra:
00:40
a couple pounds of pinkish-gray flesh,
12
25000
2000
un quilo de carne de cor gris rosácea,
00:42
amorphous --
13
27000
2000
amorfa,
00:44
but the last hundred years of neuroscience
14
29000
2000
pero os últimos cen anos de neurociencia
00:46
have allowed us to zoom in on the brain,
15
31000
2000
permitíronnos entrar no cerebro
00:48
and to see the intricacy of what lies within.
16
33000
2000
e ve-la complexidade do seu interior.
00:50
And they've told us that this brain
17
35000
2000
E dixéronnos que este cerebro
00:52
is an incredibly complicated circuit
18
37000
2000
é un circuíto incriblemente complicado
00:54
made out of hundreds of billions of cells called neurons.
19
39000
4000
composto de centos de miles de millóns
de células chamadas neuronas.
00:58
Now unlike a human-designed computer,
20
43000
3000
Agora ben, ao contrario dunha computadora
deseñada por humanos
con moitas menos pezas distintas
01:01
where there's a fairly small number of different parts --
21
46000
2000
01:03
we know how they work, because we humans designed them --
22
48000
3000
--sabemos como funcionan,
posto que as deseñamos nos--
01:06
the brain is made out of thousands of different kinds of cells,
23
51000
3000
o cerebro está composto
de miles de tipos diferentes de células,
01:09
maybe tens of thousands.
24
54000
2000
quizais decenas de miles.
De distintas formas, a partir de
moléculas diversas,
01:11
They come in different shapes; they're made out of different molecules.
25
56000
2000
01:13
And they project and connect to different brain regions,
26
58000
3000
e que proxectan e conectan
cara a distintas rexións do cerebro.
01:16
and they also change different ways in different disease states.
27
61000
3000
E tamén cambian nos diferentes estadios
da enfermidade.
01:19
Let's make it concrete.
28
64000
2000
Sexamos máis concretos.
01:21
There's a class of cells,
29
66000
2000
Temos unha clase de células,
unha célula moi pequena, inhibidora,
que silencia as súas veciñas.
01:23
a fairly small cell, an inhibitory cell, that quiets its neighbors.
30
68000
3000
01:26
It's one of the cells that seems to be atrophied in disorders like schizophrenia.
31
71000
4000
É unha das células que se atrofiaría en
trastornos como a esquizofrenia.
01:30
It's called the basket cell.
32
75000
2000
É coñecida coma célula cesta;
01:32
And this cell is one of the thousands of kinds of cell
33
77000
2000
E é un dos miles de tipos de células
01:34
that we are learning about.
34
79000
2000
acerca das cales estamos aprendendo.
01:36
New ones are being discovered everyday.
35
81000
2000
Descóbrense novos tipos tódolos días.
01:38
As just a second example:
36
83000
2000
Só un segundo exemplo:
01:40
these pyramidal cells, large cells,
37
85000
2000
estas células piramidais, grandes células,
01:42
they can span a significant fraction of the brain.
38
87000
2000
que abarcan unha parte importante
do cerebro.
01:44
They're excitatory.
39
89000
2000
Son excitatorias.
01:46
And these are some of the cells
40
91000
2000
E estas son algunhas das células
01:48
that might be overactive in disorders such as epilepsy.
41
93000
3000
que poderían estar hiperactivas
en trastornos como a epilepsia.
01:51
Every one of these cells
42
96000
2000
Cada unha destas células
01:53
is an incredible electrical device.
43
98000
3000
é un dispositivo eléctrico incrible.
01:56
They receive input from thousands of upstream partners
44
101000
2000
Reciben sinais de miles de células
da parte superior
01:58
and compute their own electrical outputs,
45
103000
3000
e calculan as súas propias
respostas eléctricas
02:01
which then, if they pass a certain threshold,
46
106000
2000
que ao superar un determinado límite,
pasarán a miles de células
da parte inferior.
02:03
will go to thousands of downstream partners.
47
108000
2000
02:05
And this process, which takes just a millisecond or so,
48
110000
3000
E este proceso, que leva só
un milisegundo máis ou menos,
02:08
happens thousands of times a minute
49
113000
2000
sucede miles de veces por minuto
02:10
in every one of your 100 billion cells,
50
115000
2000
para cada unha
das 100 mil millóns de células,
02:12
as long as you live
51
117000
2000
mentres vostedes viven,
02:14
and think and feel.
52
119000
3000
e pensan e senten.
02:17
So how are we going to figure out what this circuit does?
53
122000
3000
Así pois, como imos descifrar
qué fai este circuíto?
02:20
Ideally, we could go through the circuit
54
125000
2000
O ideal sería ir a través del
02:22
and turn these different kinds of cell on and off
55
127000
3000
e acender e apaga-los distintos
tipos de células
02:25
and see whether we could figure out
56
130000
2000
e ver se podemos esclarecer
02:27
which ones contribute to certain functions
57
132000
2000
cales contribúen a determinadas funcións
02:29
and which ones go wrong in certain pathologies.
58
134000
2000
e cales funcionan mal
en certas patoloxías.
02:31
If we could activate cells, we could see what powers they can unleash,
59
136000
3000
Se puideramos activalas poderíamos
ver qué potencial liberan,
02:34
what they can initiate and sustain.
60
139000
2000
e qué poden iniciar e manter.
02:36
If we could turn them off,
61
141000
2000
Se puideramos apagalas, entón
poderíamos tentar ver
para que son precisas.
02:38
then we could try and figure out what they're necessary for.
62
143000
2000
02:40
And that's a story I'm going to tell you about today.
63
145000
3000
E esa é unha historia
que lles vou contar hoxe.
02:43
And honestly, where we've gone through over the last 11 years,
64
148000
3000
E, honestamente, por onde pasamos
nos últimos 11 anos
02:46
through an attempt to find ways
65
151000
2000
na busca de maneiras
02:48
of turning circuits and cells and parts and pathways of the brain
66
153000
2000
de acender e apagar circuítos e células
02:50
on and off,
67
155000
2000
e rutas do cerebro,
02:52
both to understand the science
68
157000
2000
tanto para entende-la ciencia,
02:54
and also to confront some of the issues
69
159000
3000
como para facerlles fronte
a algúns dos problemas
02:57
that face us all as humans.
70
162000
3000
cos que todos batemos como humanos.
03:00
Now before I tell you about the technology,
71
165000
3000
Pero antes de falarlles da tecnoloxía,
03:03
the bad news is that a significant fraction of us in this room,
72
168000
3000
a mala nova é que unha parte
significativa de nós nesta sala,
03:06
if we live long enough,
73
171000
2000
se vivímo-lo suficiente,
03:08
will encounter, perhaps, a brain disorder.
74
173000
2000
probablemente suframos
un trastorno cerebral.
03:10
Already, a billion people
75
175000
2000
Xa mil millóns de persoas
03:12
have had some kind of brain disorder
76
177000
2000
tiveron algún tipo de trastorno cerebral
03:14
that incapacitates them,
77
179000
2000
que as incapacita,
e, porén, as cifras non lle fan xustiza.
03:16
and the numbers don't do it justice though.
78
181000
2000
03:18
These disorders -- schizophrenia, Alzheimer's,
79
183000
2000
Estes trastornos
--a esquizofrenia, o alzheimer,
03:20
depression, addiction --
80
185000
2000
a depresión, a adicción--
03:22
they not only steal our time to live, they change who we are.
81
187000
3000
non só nos rouban tempo de vida,
cambian o noso ser.
03:25
They take our identity and change our emotions
82
190000
2000
Quítannos a nosa identidade e cambian
as nosas emocións
03:27
and change who we are as people.
83
192000
3000
e cambian o que somos como persoas.
03:30
Now in the 20th century,
84
195000
3000
Agora ben, no século XX
03:33
there was some hope that was generated
85
198000
3000
xerouse algo de esperanza
03:36
through the development of pharmaceuticals for treating brain disorders,
86
201000
3000
grazas ao desenvolvemento de fármacos
para trastornos cerebrais.
03:39
and while many drugs have been developed
87
204000
3000
E aínda que se desenvolveron
moitas medicinas
que poden alivia-los síntomas
deses trastornos,
03:42
that can alleviate symptoms of brain disorders,
88
207000
2000
03:44
practically none of them can be considered to be cured.
89
209000
3000
na práctica ningún deles pode
considerarse curable.
03:47
And part of that's because we're bathing the brain in the chemical.
90
212000
3000
En parte porque estamos inundando
o cerebro con química.
03:50
This elaborate circuit
91
215000
2000
Este elaborado circuíto
composto de miles
de tipos de células diferentes
03:52
made out of thousands of different kinds of cell
92
217000
2000
03:54
is being bathed in a substance.
93
219000
2000
está sendo bañado nunha substancia.
Por iso, a maior parte das medicinas
03:56
That's also why, perhaps, most of the drugs, and not all, on the market
94
221000
2000
03:58
can present some kind of serious side effect too.
95
223000
3000
pode presentar algún tipo
de efecto secundario importante.
04:01
Now some people have gotten some solace
96
226000
3000
Agora, algunhas persoas recibiron
algún consolo
04:04
from electrical stimulators that are implanted in the brain.
97
229000
3000
de estimuladores eléctricos
que se implantan no cerebro.
04:07
And for Parkinson's disease,
98
232000
2000
E para o párkinson,
04:09
Cochlear implants,
99
234000
2000
os implantes cocleares,
04:11
these have indeed been able
100
236000
2000
foron capaces
04:13
to bring some kind of remedy
101
238000
2000
de fornecer algún alivio
a persoas con certos tipos de trastornos.
04:15
to people with certain kinds of disorder.
102
240000
2000
04:17
But electricity also will go in all directions --
103
242000
2000
Pero a electricidade
tamén irá cara a todas partes
04:19
the path of least resistance,
104
244000
2000
pola ruta de menor resistencia,
04:21
which is where that phrase, in part, comes from.
105
246000
2000
e de aí, en parte, vén esta expresión.
04:23
And it also will affect normal circuits as well as the abnormal ones that you want to fix.
106
248000
3000
E isto afectará aos circuítos normais
pero tamén aos que queremos corrixir.
04:26
So again, we're sent back to the idea
107
251000
2000
Así que de novo, volvemos á idea
04:28
of ultra-precise control.
108
253000
2000
do control ultrapreciso.
04:30
Could we dial-in information precisely where we want it to go?
109
255000
3000
Podemos dirixi-la información exactamente
cara a onde queremos?
04:34
So when I started in neuroscience 11 years ago,
110
259000
4000
Cando comecei na neurociencia hai 11 anos
04:38
I had trained as an electrical engineer and a physicist,
111
263000
3000
formárame como enxeñeiro eléctrico
e como físico,
04:41
and the first thing I thought about was,
112
266000
2000
e o primeiro que pensei foi:
se as neuronas son dispositivos eléctricos
04:43
if these neurons are electrical devices,
113
268000
2000
04:45
all we need to do is to find some way
114
270000
2000
todo o que fai falla é atopa-lo modo
de manexar a distancia
eses cambios eléctricos.
04:47
of driving those electrical changes at a distance.
115
272000
2000
04:49
If we could turn on the electricity in one cell,
116
274000
2000
Se puideramos acende-la electricidade
nunha célula,
04:51
but not its neighbors,
117
276000
2000
pero non nas veciñas,
iso daríanos o que necesitamos para
activar e apaga-las células,
04:53
that would give us the tool we need to activate and shut down these different cells,
118
278000
3000
04:56
figure out what they do and how they contribute
119
281000
2000
para descubrir que fan e como contribúen
04:58
to the networks in which they're embedded.
120
283000
2000
ás redes nas que están inseridas.
E tamén nos permitiría
o control ultrapreciso
05:00
And also it would allow us to have the ultra-precise control we need
121
285000
2000
05:02
in order to fix the circuit computations
122
287000
3000
necesario para corrixi-los cálculos
do circuíto
05:05
that have gone awry.
123
290000
2000
que estiveran mal.
05:07
Now how are we going to do that?
124
292000
2000
Agora, como imos facer iso?
05:09
Well there are many molecules that exist in nature,
125
294000
2000
Ben, na natureza haiche moitas moléculas
05:11
which are able to convert light into electricity.
126
296000
3000
capaces de converte-la luz
en electricidade.
05:14
You can think of them as little proteins
127
299000
2000
Imaxínenas como pequenas proteínas
05:16
that are like solar cells.
128
301000
2000
que son como celas fotovoltaicas.
05:18
If we can install these molecules in neurons somehow,
129
303000
3000
Se, dalgún xeito, podemos instalar estas
moléculas nas neuronas
05:21
then these neurons would become electrically drivable with light.
130
306000
3000
entón estas neuronas poderían
manipularse electricamente coa luz.
05:24
And their neighbors, which don't have the molecule, would not.
131
309000
3000
E as súas veciñas,
que non teñen a molécula, non.
05:27
There's one other magic trick you need to make this all happen,
132
312000
2000
Cómprenos outro truco de maxia
para que isto suceda:
05:29
and that's the ability to get light into the brain.
133
314000
3000
a capacidade de meter luz no cerebro.
05:32
And to do that -- the brain doesn't feel pain -- you can put --
134
317000
3000
E para logralo --o cerebro non sente dor--
pódese poñer
05:35
taking advantage of all the effort
135
320000
2000
--aproveitando o esforzo
investido en Internet,
comunicacións, etc--
05:37
that's gone into the Internet and communications and so on --
136
322000
2000
05:39
optical fibers connected to lasers
137
324000
2000
fibra óptica conectada a láseres
que pode usarse para activar,
por exemplo en modelos animais,
05:41
that you can use to activate, in animal models for example,
138
326000
2000
05:43
in pre-clinical studies,
139
328000
2000
en estudios preclínicos,
05:45
these neurons and to see what they do.
140
330000
2000
estas neuronas e ver qué fan.
05:47
So how do we do this?
141
332000
2000
Entón, como o facemos?
05:49
Around 2004,
142
334000
2000
Arredor de 2004,
en colaboración con Gerhard Nagel
e Karl Deisseroth
05:51
in collaboration with Gerhard Nagel and Karl Deisseroth,
143
336000
2000
05:53
this vision came to fruition.
144
338000
2000
esta visión fíxose realidade.
05:55
There's a certain alga that swims in the wild,
145
340000
3000
Hai unha alga que nada no mundo silvestre
05:58
and it needs to navigate towards light
146
343000
2000
e que ten que navegar cara á luz
06:00
in order to photosynthesize optimally.
147
345000
2000
para face-la fotosíntese de forma óptima.
06:02
And it senses light with a little eye-spot,
148
347000
2000
E detecta a luz cun pequeno ocelo
06:04
which works not unlike how our eye works.
149
349000
3000
que funciona non moi distinto
ca os nosos ollos.
06:07
In its membrane, or its boundary,
150
352000
2000
Na súa membrana, ou no seu bordo,
06:09
it contains little proteins
151
354000
3000
contén pequenas proteínas
06:12
that indeed can convert light into electricity.
152
357000
3000
que, de feito, poden converte-la luz
en electricidade.
06:15
So these molecules are called channelrhodopsins.
153
360000
3000
Estas moléculas denomínanse
canalrodopsinas.
06:18
And each of these proteins acts just like that solar cell that I told you about.
154
363000
3000
E cada unha actúa como esa cela
fotovoltaica da que falei.
06:21
When blue light hits it, it opens up a little hole
155
366000
3000
Ante a presenza de luz azul,
abre un pequeno orificio
06:24
and allows charged particles to enter the eye-spot,
156
369000
2000
que deixa pasar partículas cargadas
ao ocelo.
06:26
and that allows this eye-spot to have an electrical signal
157
371000
2000
o que lle permite ter un sinal eléctrico
06:28
just like a solar cell charging up a battery.
158
373000
3000
como unha cela fotovoltaica
que carga unha batería.
06:31
So what we need to do is to take these molecules
159
376000
2000
Daquela,temos que toma-las moléculas
06:33
and somehow install them in neurons.
160
378000
2000
e poñelas dalgún xeito nas neuronas.
06:35
And because it's a protein,
161
380000
2000
E dado que é unha proteína,
está codificada no ADN deste organismo.
06:37
it's encoded for in the DNA of this organism.
162
382000
3000
06:40
So all we've got to do is take that DNA,
163
385000
2000
Así que o que temos que facer é
toma-lo ADN,
06:42
put it into a gene therapy vector, like a virus,
164
387000
3000
colocalo nun vector de terapia xénica,
coma un virus,
06:45
and put it into neurons.
165
390000
3000
E poñelo nas neuronas.
06:48
So it turned out that this was a very productive time in gene therapy,
166
393000
3000
Resultou ser un momento moi produtivo
en terapia xénica,
06:51
and lots of viruses were coming along.
167
396000
2000
e empezaron a aparecer moitos virus.
06:53
So this turned out to be very simple to do.
168
398000
2000
Así que resultou moi simple de facer.
06:55
And early in the morning one day in the summer of 2004,
169
400000
3000
E unha mañanciña do verán de 2004
tentámolo e funcionou á primeira.
06:58
we gave it a try, and it worked on the first try.
170
403000
2000
07:00
You take this DNA and you put it into a neuron.
171
405000
3000
Tómase este ADN e colócase nunha neurona.
07:03
The neuron uses its natural protein-making machinery
172
408000
3000
A neurona usa o seu mecanismo natural
de elaboración de proteínas
07:06
to fabricate these little light-sensitive proteins
173
411000
2000
para facer pequenas proteínas
fotosensibles
07:08
and install them all over the cell,
174
413000
2000
e colocalas por toda a célula,
07:10
like putting solar panels on a roof,
175
415000
2000
como paneis solares nun tellado,
07:12
and the next thing you know,
176
417000
2000
e o seguinte que sabemos
é que temos unha neurona
activable por luz.
07:14
you have a neuron which can be activated with light.
177
419000
2000
07:16
So this is very powerful.
178
421000
2000
E isto é moi valioso.
07:18
One of the tricks you have to do
179
423000
2000
Un dos trucos que tes que facer
é atopar como leva-los xenes
ás células que queres
07:20
is to figure out how to deliver these genes to the cells that you want
180
425000
2000
07:22
and not all the other neighbors.
181
427000
2000
E non a tódalas súas veciñas.
07:24
And you can do that; you can tweak the viruses
182
429000
2000
E pode facerse; pódese axusta-lo virus
para que acade unhas células e non outras.
07:26
so they hit just some cells and not others.
183
431000
2000
07:28
And there's other genetic tricks you can play
184
433000
2000
E hai outros trucos xenéticos
aos que recorrer
07:30
in order to get light-activated cells.
185
435000
3000
co fin de obter células fotoactivadas.
07:33
This field has now come to be known as optogenetics.
186
438000
4000
Este campo coñécese como optoxenética.
E, como exemplo de cousas
que se poden facer,
07:37
And just as one example of the kind of thing you can do,
187
442000
2000
07:39
you can take a complex network,
188
444000
2000
podes tomar unha rede complexa,
07:41
use one of these viruses to deliver the gene
189
446000
2000
usar un destes virus para entrega-lo xene
07:43
just to one kind of cell in this dense network.
190
448000
3000
a un só tipo de célula nesta densa rede.
07:46
And then when you shine light on the entire network,
191
451000
2000
E despois cando a luz ilumina toda a rede
07:48
just that cell type will be activated.
192
453000
2000
só se activará ese tipo de célula.
07:50
So for example, lets sort of consider that basket cell I told you about earlier --
193
455000
3000
Por exemplo, pensemos nesa célula
cesta que lles mencionei antes,
07:53
the one that's atrophied in schizophrenia
194
458000
2000
a que se atrofia na esquizofrenia
07:55
and the one that is inhibitory.
195
460000
2000
e que é inhibitoria.
07:57
If we can deliver that gene to these cells --
196
462000
2000
Se podemos levar ese xene a esas células
07:59
and they're not going to be altered by the expression of the gene, of course --
197
464000
3000
e que non se vexan alteradas pola
expresión dese xene, por suposto,
08:02
and then flash blue light over the entire brain network,
198
467000
3000
e despois iluminamos de azul
toda a rede cerebral
08:05
just these cells are going to be driven.
199
470000
2000
só se verán afectadas esas células.
08:07
And when the light turns off, these cells go back to normal,
200
472000
2000
E cando apagamos a luz as células
volven á normalidade,
08:09
so they don't seem to be averse against that.
201
474000
3000
así que iso non parece afectalas
.
08:12
Not only can you use this to study what these cells do,
202
477000
2000
Non só se usa para estuda-lo
funcionamento celular,
08:14
what their power is in computing in the brain,
203
479000
2000
o seu poder de cómputo no cerebro,
08:16
but you can also use this to try to figure out --
204
481000
2000
senón tamén para tratar de descubrir
08:18
well maybe we could jazz up the activity of these cells,
205
483000
2000
se poderiamos animar
a actividade desas células
08:20
if indeed they're atrophied.
206
485000
2000
se realmente están atrofiadas.
08:22
Now I want to tell you a couple of short stories
207
487000
2000
Agora quero contarlles
un par de historias breves
08:24
about how we're using this,
208
489000
2000
acerca do uso que facemos disto,
08:26
both at the scientific, clinical and pre-clinical levels.
209
491000
3000
a nivel científico, clínico e preclínico.
08:29
One of the questions we've confronted
210
494000
2000
Unha das preguntas a que nos enfrontamos é
08:31
is, what are the signals in the brain that mediate the sensation of reward?
211
496000
3000
cales son os sinais cerebrais implicados
na sensación de recompensa?
08:34
Because if you could find those,
212
499000
2000
Porque se puideramos atopalos
serían os sinais que poderían
guia-la aprendizaxe.
08:36
those would be some of the signals that could drive learning.
213
501000
2000
08:38
The brain will do more of whatever got that reward.
214
503000
2000
O cerebro repetirá o que o gratifica.
08:40
And also these are signals that go awry in disorders such as addiction.
215
505000
3000
E ademais, estes sinais funcionan mal
nos trastornos adictivos.
08:43
So if we could figure out what cells they are,
216
508000
2000
Así, se descubrísemos esas células
08:45
we could maybe find new targets
217
510000
2000
quizais poderiamos atopar novas dianas
08:47
for which drugs could be designed or screened against,
218
512000
2000
para as que deseñar
ou probar medicamentos,
08:49
or maybe places where electrodes could be put in
219
514000
2000
ou quizais lugares
nos que colocar eléctrodos
08:51
for people who have very severe disability.
220
516000
3000
para persoas con discapacidades
moi graves.
08:54
So to do that, we came up with a very simple paradigm
221
519000
2000
Para isto, ocorréusenos
un paradigma moi simple
08:56
in collaboration with the Fiorella group,
222
521000
2000
en colaboración co grupo Fiorella,
08:58
where one side of this little box,
223
523000
2000
nun lado desta pequena caixa,
09:00
if the animal goes there, the animal gets a pulse of light
224
525000
2000
se o animal vai alí,
recibe un pulso de luz
09:02
in order to make different cells in the brain sensitive to light.
225
527000
2000
que fará fotosensibles
varias células cerebrais.
09:04
So if these cells can mediate reward,
226
529000
2000
Así, se estas células
participan na recompensa,
09:06
the animal should go there more and more.
227
531000
2000
o animal debería ir alí cada vez máis.
09:08
And so that's what happens.
228
533000
2000
Iso é o que sucede.
O animal vai ir mete-lo nariz
ao lado dereito
09:10
This animal's going to go to the right-hand side and poke his nose there,
229
535000
2000
09:12
and he gets a flash of blue light every time he does that.
230
537000
2000
e cada vez que o fai recibe un
flash azul.
09:14
And he'll do that hundreds and hundreds of times.
231
539000
2000
E farao centos e centos de veces.
09:16
These are the dopamine neurons,
232
541000
2000
Son as neuronas dopamina,
que como saberán, participan
dos centros cerebrais do pracer.
09:18
which some of you may have heard about, in some of the pleasure centers in the brain.
233
543000
2000
09:20
Now we've shown that a brief activation of these
234
545000
2000
Demostramos que activalas brevemente
09:22
is enough, indeed, to drive learning.
235
547000
2000
é suficiente para guia-la aprendizaxe.
09:24
Now we can generalize the idea.
236
549000
2000
Podemos xeneraliza-la idea.
09:26
Instead of one point in the brain,
237
551000
2000
En lugar de un só punto no cerebro
09:28
we can devise devices that span the brain,
238
553000
2000
podemos idear dispositivos
para todo o cerebro,
09:30
that can deliver light into three-dimensional patterns --
239
555000
2000
que leven a luz en patróns tridimensionais
09:32
arrays of optical fibers,
240
557000
2000
--matrices de fibra óptica,
09:34
each coupled to its own independent miniature light source.
241
559000
2000
cunha minifonte luminosa independente.
09:36
And then we can try to do things in vivo
242
561000
2000
E podemos tratar de facer en vivo
09:38
that have only been done to-date in a dish --
243
563000
3000
o que, ata o momento,
se fixo só nunha placa,
09:41
like high-throughput screening throughout the entire brain
244
566000
2000
como a visualización completa do cerebro
para sinais que fan
que sucedan certas cousas.
09:43
for the signals that can cause certain things to happen.
245
568000
2000
09:45
Or that could be good clinical targets
246
570000
2000
Ou poderían ser bos obxectivos clínicos
09:47
for treating brain disorders.
247
572000
2000
para o tratamento de trastornos cerebrais.
09:49
And one story I want to tell you about
248
574000
2000
E outra historia que quero contarlles
09:51
is how can we find targets for treating post-traumatic stress disorder --
249
576000
3000
é como atopamos dianas para trata-lo
estrés postraumático,
09:54
a form of uncontrolled anxiety and fear.
250
579000
3000
unha forma de ansiedade
e medo descontrolados.
09:57
And one of the things that we did
251
582000
2000
E unha das cousas que fixemos
09:59
was to adopt a very classical model of fear.
252
584000
3000
foi adopta-lo modelo máis clásico do medo.
10:02
This goes back to the Pavlovian days.
253
587000
3000
Remóntase aos días de Pavlov.
10:05
It's called Pavlovian fear conditioning --
254
590000
2000
Denomínase medo condicionado pavloviano,
10:07
where a tone ends with a brief shock.
255
592000
2000
e nel un ton remata cunha breve descarga.
10:09
The shock isn't painful, but it's a little annoying.
256
594000
2000
A descarga non doe, pero molesta un pouco.
10:11
And over time -- in this case, a mouse,
257
596000
2000
E co tempo --un rato,
un bo modelo animal,
usado a miúdo en experimentos,
10:13
which is a good animal model, commonly used in such experiments --
258
598000
2000
10:15
the animal learns to fear the tone.
259
600000
2000
aprende a temerlle ao ton.
10:17
The animal will react by freezing,
260
602000
2000
O animal reaccionará paralizándose,
10:19
sort of like a deer in the headlights.
261
604000
2000
coma un cervo diante dos faros.
10:21
Now the question is, what targets in the brain can we find
262
606000
3000
Agora, a pregunta é, que dianas
podemos atopar no cerebro
10:24
that allow us to overcome this fear?
263
609000
2000
que nos permitan superar ese temor?
10:26
So what we do is we play that tone again
264
611000
2000
Para iso reproducimos o ton novamente
10:28
after it's been associated with fear.
265
613000
2000
despois de que se asocie co medo.
10:30
But we activate targets in the brain, different ones,
266
615000
2000
Pero activamos novas dianas no cerebro,
10:32
using that optical fiber array I told you about in the previous slide,
267
617000
3000
usando esa matriz de fibra óptica
que lles mostrei antes
10:35
in order to try and figure out which targets
268
620000
2000
para tratar de desvelar qué dianas
10:37
can cause the brain to overcome that memory of fear.
269
622000
3000
poden facer que o cerebro supere
esa memoria do medo.
10:40
And so this brief video
270
625000
2000
Este breve vídeo
mostra unha das dianas
con que traballamos.
10:42
shows you one of these targets that we're working on now.
271
627000
2000
10:44
This is an area in the prefrontal cortex,
272
629000
2000
Unha área do córtex prefrontal,
unha rexión
10:46
a region where we can use cognition to try to overcome aversive emotional states.
273
631000
3000
na que pode usarse a cognición para
superar estados aversivos.
10:49
And the animal's going to hear a tone -- and a flash of light occurred there.
274
634000
2000
Cando o animal oe un ton,
aparece un flash.
10:51
There's no audio on this, but you can see the animal's freezing.
275
636000
2000
Non o oen, pero ven
que o rato se paraliza.
10:53
This tone used to mean bad news.
276
638000
2000
O ton adoitaba representar malas noticias.
10:55
And there's a little clock in the lower left-hand corner,
277
640000
2000
Hai un reloxo na esquina inferior esquerda
10:57
so you can see the animal is about two minutes into this.
278
642000
3000
para que poidan ver que o animal
queda así uns 2 minutos.
11:00
And now this next clip
279
645000
2000
E agora, o seguinte vídeo,
11:02
is just eight minutes later.
280
647000
2000
de só 8 minutos despois.
11:04
And the same tone is going to play, and the light is going to flash again.
281
649000
3000
Vaise reproduci-lo ton, e a luz
dispárase outra vez.
11:07
Okay, there it goes. Right now.
282
652000
3000
Ben, aí vai. Xusto agora.
11:10
And now you can see, just 10 minutes into the experiment,
283
655000
3000
E agora poden ver,
en só 10 minutos de experimento,
11:13
that we've equipped the brain by photoactivating this area
284
658000
3000
que preparamos o cerebro,
fotoactivando esta zona,
11:16
to overcome the expression
285
661000
2000
para supera-la expresión
11:18
of this fear memory.
286
663000
2000
desta memoria do medo.
11:20
Now over the last couple of years, we've gone back to the tree of life
287
665000
3000
Durante os últimos dous anos,
volvemos atrás na árbore da vida
11:23
because we wanted to find ways to turn circuits in the brain off.
288
668000
3000
porque queriamos atopar modos de
apaga-los circuítos cerebrais.
11:26
If we could do that, this could be extremely powerful.
289
671000
3000
Se puideramos facelo sería
algo moi poderoso.
11:29
If you can delete cells just for a few milliseconds or seconds,
290
674000
3000
Se podes suprimir células
durante uns milisegundos ou segundos,
11:32
you can figure out what necessary role they play
291
677000
2000
podes darte de conta da súa relevancia
11:34
in the circuits in which they're embedded.
292
679000
2000
nos circuítos en que están inseridas.
11:36
And we've now surveyed organisms from all over the tree of life --
293
681000
2000
E estudamos toda a árbore da vida,
seres de tódolos reinos salvo o animal,
que é levemente distinto.
11:38
every kingdom of life except for animals, we see slightly differently.
294
683000
3000
11:41
And we found all sorts of molecules, they're called halorhodopsins or archaerhodopsins,
295
686000
3000
E atopamos moléculas chamadas
halorrodopsinas ou arqueorrodopsinas,
11:44
that respond to green and yellow light.
296
689000
2000
que responden á luz verde e amarela.
11:46
And they do the opposite thing of the molecule I told you about before
297
691000
2000
E que fan o oposto da molécula anterior,
11:48
with the blue light activator channelrhodopsin.
298
693000
3000
a do activador de luz azul,
a canalrodopsina.
11:52
Let's give an example of where we think this is going to go.
299
697000
3000
Vexamos un exemplo de cara a onde
pensamos que vai isto.
11:55
Consider, for example, a condition like epilepsy,
300
700000
3000
Consideren, por exemplo, unha doenza
como a epilepsia,
11:58
where the brain is overactive.
301
703000
2000
na que o cerebro é hiperactivo.
12:00
Now if drugs fail in epileptic treatment,
302
705000
2000
Se falla a medicación
no tratamento da epilepsia,
12:02
one of the strategies is to remove part of the brain.
303
707000
2000
pode eliminarse parte do cerebro.
12:04
But that's obviously irreversible, and there could be side effects.
304
709000
2000
Pero isto é irreversible e ten
efectos secundarios.
12:06
What if we could just turn off that brain for a brief amount of time,
305
711000
3000
Que pasaría se puideramos apaga-lo
cerebro por un breve instante
12:09
until the seizure dies away,
306
714000
3000
ata que o ataque esvaecera
e facer que o cerebro volvera
ao seu estado inicial?
12:12
and cause the brain to be restored to its initial state --
307
717000
3000
12:15
sort of like a dynamical system that's being coaxed down into a stable state.
308
720000
3000
Algo así coma un sistema dinámico
dirixido cara a un estado estable.
12:18
So this animation just tries to explain this concept
309
723000
3000
Esta animación tenta
explicar este concepto
12:21
where we made these cells sensitive to being turned off with light,
310
726000
2000
onde creamos células
sensibles a desactivarse coa luz,
12:23
and we beam light in,
311
728000
2000
e enfocámo-la luz sobre elas,
12:25
and just for the time it takes to shut down a seizure,
312
730000
2000
e só durante o tempo que dura a convulsión
12:27
we're hoping to be able to turn it off.
313
732000
2000
esperamos ser capaces de apagalas.
12:29
And so we don't have data to show you on this front,
314
734000
2000
Non temos datos para amosar neste campo,
12:31
but we're very excited about this.
315
736000
2000
pero estamos moi ilusionados.
12:33
Now I want to close on one story,
316
738000
2000
Agora quero rematar cunha historia,
12:35
which we think is another possibility --
317
740000
2000
que nos parece outra posibilidade
12:37
which is that maybe these molecules, if you can do ultra-precise control,
318
742000
2000
e que, se acadamos
un control ultrapreciso,
12:39
can be used in the brain itself
319
744000
2000
talvez permita usar
estas moléculas no propio cerebro
12:41
to make a new kind of prosthetic, an optical prosthetic.
320
746000
3000
para facer un novo tipo de prótese óptica.
12:44
I already told you that electrical stimulators are not uncommon.
321
749000
3000
Xa dixen que os estimuladores
eléctricos son comúns hoxe.
12:47
Seventy-five thousand people have Parkinson's deep-brain stimulators implanted.
322
752000
3000
75 mil persoas teñen estimuladores
cerebrais para o párkinson.
12:50
Maybe 100,000 people have Cochlear implants,
323
755000
2000
Talvez 100 mil teñan implantes cocleares,
12:52
which allow them to hear.
324
757000
2000
que lles permiten oír.
12:54
There's another thing, which is you've got to get these genes into cells.
325
759000
3000
Outra cosa é que temos que meter
eses xenes nas células.
12:57
And new hope in gene therapy has been developed
326
762000
3000
E xurdiu unha nova esperanza en
terapia xénica
13:00
because viruses like the adeno-associated virus,
327
765000
2000
grazas a virus como o virus adenoasociado,
13:02
which probably most of us around this room have,
328
767000
2000
que probablemente
a maioría de vostedes terá
13:04
and it doesn't have any symptoms,
329
769000
2000
sen presentar síntomas,
13:06
which have been used in hundreds of patients
330
771000
2000
e que se empregou en centos de pacientes
13:08
to deliver genes into the brain or the body.
331
773000
2000
para repartir xenes no cerebro e no corpo.
13:10
And so far, there have not been serious adverse events
332
775000
2000
E, ata o de agora, non houbo
efectos adversos graves
13:12
associated with the virus.
333
777000
2000
asociados co virus.
13:14
There's one last elephant in the room, the proteins themselves,
334
779000
3000
Tamén adoitamos ignorar
outro gran problema: as propias proteínas,
13:17
which come from algae and bacteria and fungi,
335
782000
2000
procedentes de algas, bacterias e fungos,
13:19
and all over the tree of life.
336
784000
2000
e toda a árbore da vida.
13:21
Most of us don't have fungi or algae in our brains,
337
786000
2000
A maioría non temos fungos nin algas
no cerebro,
13:23
so what is our brain going to do if we put that in?
338
788000
2000
así que, que fará o cerebro se llas
inserimos?
13:25
Are the cells going to tolerate it? Will the immune system react?
339
790000
2000
Tolerarano as células?
E o sistema inmunolóxico?
13:27
In its early days -- these have not been done on humans yet --
340
792000
2000
Estamos comezando,
non se probou en humanos
13:29
but we're working on a variety of studies
341
794000
2000
pero estamos a traballar en varios estudos
13:31
to try and examine this,
342
796000
2000
tratando de examinalo,
13:33
and so far we haven't seen overt reactions of any severity
343
798000
3000
e, ata o de agora, non atopamos
reaccións graves
13:36
to these molecules
344
801000
2000
cara a estas moléculas
13:38
or to the illumination of the brain with light.
345
803000
3000
ou cara á iluminación do cerebro con luz.
13:41
So it's early days, to be upfront, but we're excited about it.
346
806000
3000
Sinceramente, estamos comezando
pero estamos entusiasmados.
13:44
I wanted to close with one story,
347
809000
2000
Quero rematar cunha historia,
13:46
which we think could potentially
348
811000
2000
que, cremos, podería
13:48
be a clinical application.
349
813000
2000
ser unha aplicación clínica.
13:50
Now there are many forms of blindness
350
815000
2000
Hoxe en día hai moitas formas de cegueira
13:52
where the photoreceptors,
351
817000
2000
nas que os fotorreceptores,
13:54
our light sensors that are in the back of our eye, are gone.
352
819000
3000
os sensores de luz que están
no fondo do ollo, non funcionan.
13:57
And the retina, of course, is a complex structure.
353
822000
2000
E a retina é unha estrutura complexa.
13:59
Now let's zoom in on it here, so we can see it in more detail.
354
824000
2000
Agora ampliémolo, para velo mellor.
14:01
The photoreceptor cells are shown here at the top,
355
826000
3000
As células fotorreceptoras
amósanse aquí arriba
14:04
and then the signals that are detected by the photoreceptors
356
829000
2000
e os sinais detectados por elas
14:06
are transformed by various computations
357
831000
2000
son transformados por varios cálculos
14:08
until finally that layer of cells at the bottom, the ganglion cells,
358
833000
3000
ata que, ao final, a capa de células
ganglionares de abaixo
14:11
relay the information to the brain,
359
836000
2000
transmite a información ao cerebro,
14:13
where we see that as perception.
360
838000
2000
onde vemos iso como percepción.
14:15
In many forms of blindness, like retinitis pigmentosa,
361
840000
3000
En moitas formas de cegueira,
como a retinite pigmentosa,
14:18
or macular degeneration,
362
843000
2000
ou a dexeneración macular,
14:20
the photoreceptor cells have atrophied or been destroyed.
363
845000
3000
as células fotorreceptoras
atrofiáronse ou destruíronse.
14:23
Now how could you repair this?
364
848000
2000
Como arranxar isto?
14:25
It's not even clear that a drug could cause this to be restored,
365
850000
3000
Nin sequera está claro
que un fármaco poida facelo
14:28
because there's nothing for the drug to bind to.
366
853000
2000
porque non ten nada ao que ligarse.
14:30
On the other hand, light can still get into the eye.
367
855000
2000
Por outra banda, a luz
aínda pode entrar no ollo.
14:32
The eye is still transparent and you can get light in.
368
857000
3000
O ollo aínda é transparente
e permite o paso da luz.
14:35
So what if we could just take these channelrhodopsins and other molecules
369
860000
3000
Así que, e se collemos
as canalrodopsinas e outras moléculas
14:38
and install them on some of these other spare cells
370
863000
2000
e as poñemos nalgunha destoutras
células libres
14:40
and convert them into little cameras.
371
865000
2000
e as convertemos en pequenas cámaras?
14:42
And because there's so many of these cells in the eye,
372
867000
2000
E, xa que hai moitas
destas células no ollo,
14:44
potentially, they could be very high-resolution cameras.
373
869000
3000
potencialmente, poderían ser cámaras
de moi alta definición.
14:47
So this is some work that we're doing.
374
872000
2000
Velaquí o noso traballo en curso.
14:49
It's being led by one of our collaborators,
375
874000
2000
Está dirixido por un
dos nosos colaboradores,
14:51
Alan Horsager at USC,
376
876000
2000
Alan Horsager na USC,
14:53
and being sought to be commercialized by a start-up company Eos Neuroscience,
377
878000
3000
e procuramos que sexa comercializado
por unha empresa nova,
14:56
which is funded by the NIH.
378
881000
2000
Eos Neuroscience, financiada polo NIH.
14:58
And what you see here is a mouse trying to solve a maze.
379
883000
2000
Aquí vemos un rato tentando
saír dun labirinto.
15:00
It's a six-arm maze. And there's a bit of water in the maze
380
885000
2000
É un labirinto de 6 brazos con auga
para que o rato se mova
e non quede sentado.
15:02
to motivate the mouse to move, or he'll just sit there.
381
887000
2000
15:04
And the goal, of course, of this maze
382
889000
2000
O obxectivo deste labirinto, por suposto,
15:06
is to get out of the water and go to a little platform
383
891000
2000
é saír da auga e subir a unha plataforma
15:08
that's under the lit top port.
384
893000
2000
baixo a comporta superior iluminada.
15:10
Now mice are smart, so this mouse solves the maze eventually,
385
895000
3000
Hoxe os ratos son listos, así que este
sae do labirinto,
15:13
but he does a brute-force search.
386
898000
2000
pero busca por forza bruta.
Nada por tódalas vías ata que,
finalmente, chega á plataforma.
15:15
He's swimming down every avenue until he finally gets to the platform.
387
900000
3000
15:18
So he's not using vision to do it.
388
903000
2000
E non está usando a visión para logralo.
15:20
These different mice are different mutations
389
905000
2000
Estes ratos son mutacións distintas
que sintetizan distintos tipos de cegueira
que afectan aos humanos.
15:22
that recapitulate different kinds of blindness that affect humans.
390
907000
3000
15:25
And so we're being careful in trying to look at these different models
391
910000
3000
Por iso pomos moito coidado cando
observamos aos nosos modelos
de xeito que chegamos
a un enfoque xeneralizado.
15:28
so we come up with a generalized approach.
392
913000
2000
15:30
So how are we going to solve this?
393
915000
2000
Así que, como imos resolver isto?
15:32
We're going to do exactly what we outlined in the previous slide.
394
917000
2000
Faremos o que esbozamos
na outra diapositiva.
15:34
We're going to take these blue light photosensors
395
919000
2000
Imos coller estes fotosensores de luz azul
15:36
and install them on a layer of cells
396
921000
2000
e ímolos instalar nunha capa de células
15:38
in the middle of the retina in the back of the eye
397
923000
3000
no medio da retina
na parte posterior do ollo
15:41
and convert them into a camera --
398
926000
2000
para convertela nunha cámara
como se instalásemos
15:43
just like installing solar cells all over those neurons
399
928000
2000
celas fotovoltaicas nas neuronas
15:45
to make them light sensitive.
400
930000
2000
para facelas sensibles á luz.
15:47
Light is converted to electricity on them.
401
932000
2000
Nelas a luz convértese en electricidade.
15:49
So this mouse was blind a couple weeks before this experiment
402
934000
3000
Así que este rato era cego un par
de semanas antes do experimento
15:52
and received one dose of this photosensitive molecule in a virus.
403
937000
3000
e recibiu unha dose desta molécula
fotosensible nun virus.
15:55
And now you can see, the animal can indeed avoid walls
404
940000
2000
E agora poden velo, o animal evita paredes
15:57
and go to this little platform
405
942000
2000
e vai a esa pequena plataforma
15:59
and make cognitive use of its eyes again.
406
944000
3000
e fai novamente
un uso cognitivo dos seus ollos.
16:02
And to point out the power of this:
407
947000
2000
E para destaca-lo poder que ten isto:
16:04
these animals are able to get to that platform
408
949000
2000
os animais poden chegar á plataforma
16:06
just as fast as animals that have seen their entire lives.
409
951000
2000
tan rápido coma os que viron toda a vida.
16:08
So this pre-clinical study, I think,
410
953000
2000
Por iso penso que este estudo preclínico
16:10
bodes hope for the kinds of things
411
955000
2000
é un bo presaxio para o tipo de cousas
16:12
we're hoping to do in the future.
412
957000
2000
que esperamos facer no futuro.
16:14
To close, I want to point out that we're also exploring
413
959000
3000
Para acabar, quero sinalar
que tamén estamos a explorar
16:17
new business models for this new field of neurotechnology.
414
962000
2000
novos negocios neste campo
da neurotecnoloxía.
16:19
We're developing these tools,
415
964000
2000
Desenvolvemos estas ferramentas,
16:21
but we share them freely with hundreds of groups all over the world,
416
966000
2000
pero compartímolas con grupos
de todo o mundo
16:23
so people can study and try to treat different disorders.
417
968000
2000
de xeito que se estudan e tratan
moitos trastornos.
16:25
And our hope is that, by figuring out brain circuits
418
970000
3000
E esperamos que, ao entender
os circuítos cerebrais
16:28
at a level of abstraction that lets us repair them and engineer them,
419
973000
3000
a un nivel de abstracción que nos
permita reparalos e deseñalos,
16:31
we can take some of these intractable disorders that I told you about earlier,
420
976000
3000
poidamos tomar algún dos trastornos
incurables dos que lles falei,
16:34
practically none of which are cured,
421
979000
2000
practicamente ningún deles ten cura,
16:36
and in the 21st century make them history.
422
981000
2000
e facer que no século XXI sexan historia.
16:38
Thank you.
423
983000
2000
Grazas.
16:40
(Applause)
424
985000
13000
(Aplausos)
16:53
Juan Enriquez: So some of the stuff is a little dense.
425
998000
3000
Juan Enriquez: Algunhas das cousas
son un pouco densas.
16:56
(Laughter)
426
1001000
2000
(Risas)
16:58
But the implications
427
1003000
2000
Pero as consecuencias
17:00
of being able to control seizures or epilepsy
428
1005000
3000
de poder controla-las convulsións
ou a epilepsia
17:03
with light instead of drugs,
429
1008000
2000
con luz en vez de medicamentos,
17:05
and being able to target those specifically
430
1010000
3000
e poder identificalos especificamente
17:08
is a first step.
431
1013000
2000
é un primeiro paso.
17:10
The second thing that I think I heard you say
432
1015000
2000
Outra cosa que creo que dixeches
17:12
is you can now control the brain in two colors,
433
1017000
3000
é que agora ti podes controla-lo cerebro
con dúas cores,
17:15
like an on/off switch.
434
1020000
2000
como un interruptor de acender/apagar.
17:17
Ed Boyden: That's right.
435
1022000
2000
Ed Boyden: Correcto.
17:19
JE: Which makes every impulse going through the brain a binary code.
436
1024000
3000
JE: O que transforma cada impulso cerebral
nun código binario.
17:22
EB: Right, yeah.
437
1027000
2000
EB: Correcto, si.
17:24
So with blue light, we can drive information, and it's in the form of a one.
438
1029000
3000
Coa luz azul, podemos
conduci-la información en forma de un.
17:27
And by turning things off, it's more or less a zero.
439
1032000
2000
E apagándoa sería, máis ou
menos, un cero.
17:29
So our hope is to eventually build brain coprocessors
440
1034000
2000
Así esperamos, ao final, construír
17:31
that work with the brain
441
1036000
2000
procesadores cerebrais que
funcionen co cerebro
17:33
so we can augment functions in people with disabilities.
442
1038000
3000
para poder aumenta-las función das persoas
con discapacidade.
17:36
JE: And in theory, that means that,
443
1041000
2000
JE: E en teoría, iso significa que,
17:38
as a mouse feels, smells,
444
1043000
2000
o xeito en que un rato sente, ole,
17:40
hears, touches,
445
1045000
2000
oe, toca,
17:42
you can model it out as a string of ones and zeros.
446
1047000
3000
ti podes modelalo como unha cadea
de uns e ceros.
17:45
EB: Sure, yeah. We're hoping to use this as a way of testing
447
1050000
2000
EB: Sí, claro. Esperamos usalo
para comprobar
17:47
what neural codes can drive certain behaviors
448
1052000
2000
qué códigos neurais guían certos
comportamentos,
17:49
and certain thoughts and certain feelings,
449
1054000
2000
e certos pensamentos e sentimentos,
17:51
and use that to understand more about the brain.
450
1056000
3000
e para entender máis sobre o cerebro.
17:54
JE: Does that mean that some day you could download memories
451
1059000
3000
JE: Significa iso que algún día
poderanse descargar recordos
17:57
and maybe upload them?
452
1062000
2000
e quizais actualizalos?
EB: Ben, estamos empezando
a traballar a fondo niso.
17:59
EB: Well that's something we're starting to work on very hard.
453
1064000
2000
18:01
We're now working on some work
454
1066000
2000
Agora estamos traballando
en revesti-lo cerebro
con elementos de gravación.
18:03
where we're trying to tile the brain with recording elements too.
455
1068000
2000
18:05
So we can record information and then drive information back in --
456
1070000
3000
Así, poderemos gravar información
e despois recuperala
18:08
sort of computing what the brain needs
457
1073000
2000
como calculando o que necesita o cerebro
18:10
in order to augment its information processing.
458
1075000
2000
para aumenta-lo seu procesamento
de información.
18:12
JE: Well, that might change a couple things. Thank you. (EB: Thank you.)
459
1077000
3000
JE: Ben, iso podería cambiar algunhas
cousas. Grazas.
EB: Grazas.
18:15
(Applause)
460
1080000
3000
(Aplausos)
Translated by Luz Leirós
Reviewed by Servizo de Normalización Lingüística

▲Back to top

ABOUT THE SPEAKER
Ed Boyden - Neuroengineer
Ed Boyden is a professor of biological engineering and brain and cognitive sciences at the MIT Media Lab and the MIT McGovern Institute.

Why you should listen

Ed Boyden leads the Synthetic Neurobiology Group, which develops tools for analyzing and repairing complex biological systems such as the brain. His group applies these tools in a systematic way in order to reveal ground truth scientific understandings of biological systems, which in turn reveal radical new approaches for curing diseases and repairing disabilities. These technologies include expansion microscopy, which enables complex biological systems to be imaged with nanoscale precision, and optogenetic tools, which enable the activation and silencing of neural activity with light (TED Talk: A light switch for neurons). Boyden also co-directs the MIT Center for Neurobiological Engineering, which aims to develop new tools to accelerate neuroscience progress.

Amongst other recognitions, Boyden has received the Breakthrough Prize in Life Sciences (2016), the BBVA Foundation Frontiers of Knowledge Award (2015), the Carnegie Prize in Mind and Brain Sciences (2015), the Jacob Heskel Gabbay Award (2013), the Grete Lundbeck Brain Prize (2013) and the NIH Director's Pioneer Award (2013). He was also named to the World Economic Forum Young Scientist list (2013) and the Technology Review World's "Top 35 Innovators under Age 35" list (2006). His group has hosted hundreds of visitors to learn how to use new biotechnologies and spun out several companies to bring inventions out of his lab and into the world. Boyden received his Ph.D. in neurosciences from Stanford University as a Hertz Fellow, where he discovered that the molecular mechanisms used to store a memory are determined by the content to be learned. Before that, he received three degrees in electrical engineering, computer science and physics from MIT. He has contributed to over 300 peer-reviewed papers, current or pending patents and articles, and he has given over 300 invited talks on his group's work.

More profile about the speaker
Ed Boyden | Speaker | TED.com
THE ORIGINAL VIDEO ON TED.COM