Let's say we wanted a robot arm, something simple,
to do something for us-- you know, like scratch your head.
Seems innocuous, right?
Just do this.
Nuh-uh.
It's way more complicated than that.
Not only do I have to move my arm at the correct angle,
I have to move it to the right spot on my head--
the exact spot, which is really important when
you got an itch, which can be hard with a built-in arm, let
alone one that isn't attached to your body directly.
Then you have to get to that spot
and apply pressure, but not too much pressure, because you
don't want to rip your skin, and you don't
want to puncture your skull.
This is a robot arm, remember.
It's made of metal and motors, so there's
a lot to think about and a lot to worry about.
We need to know what the brain is telling itself,
what language the brain is speaking to itself,
and we need the computer that you're connecting this robot
arm through to understand all of that
before we can even approach the idea of putting it
near my skull.
Isn't that crazy to think about?
Yeah.
BCIs, or brain computer interfaces,
are really complicated because you're talking about brains.
What is the language of the brain itself?
Is it binary like computers?
Is it the language of the user?
Do Spanish brains speak in Spanish?
Or Chinese brains in Chinese?
English brains in English?
French brains in French?
Does the brain have a universal language?
Think back to that man moving a rat's tail from earlier.
Is that proof that one brain and another
speak the same language?
Not really, but the brain does have a language,
and we are learning it.
So let's kick into this one and figure it out.
Sup, brainos.
This is episode two of five on upgrading our brain organs.
So make sure that you stick around for all the episodes.
Go back if you haven't seen episode one.
Here we go.
In 1888, Santiago Ramon y Cajal, the Spanish neuroscientist
who has the best name ever, discovered the neuron.
Well, he actually did it in 1887, published it in 1888,
and then he won the Nobel Prize in physiology and medicine
in 1906.
And he did it for this thing called neuron theory.
Basically, neuron theory is the same today
as it was a century ago, and Jessica has a little model.
This is my model of a neuron, and this is
kind of the body of the neuron.
And this is like the wire.
And this is where the information gets sent to.
And so when the body of the neuron
decides it wants to send information,
it will send an electrical impulse this way,
and that electrical impulse creates a magnetic field
and an electrical field that can be picked up
using an electrode that is in the vicinity of that neuron.
And so what you have is a lot of these neurons in your brain
that create a lot of activity that we
can see using electrodes, but the farther
away your electrode is, the harder
it is to pick up those signals.
And so what we want to do is figure out,
what is the activity that we're looking for?
And then how can we record it?
The brain definitely communicates
in electricity and related ions, because the ions
are generated through electricity and hormones.
And then on top of that, you have high frequency spikes
going on.
So the spikes are little bursts of electricity.
Since everything's electric, and it works on basically
a threshold, there's a go/no-go threshold,
one spike down here that maybe that spike wouldn't have
happened unless your hormones had boosted the threshold
a little bit.
And so your hormones make you more [INAUDIBLE]
in certain areas and less in others.
So the brain's language isn't just one thing.
It's a combination of electricity and hormones.
That's Sharlene Flesher, by the way.
I called her because she looks at brains
from the side of an engineer and not a biologist.
Hi, I'm Sharlene Flesher.
I've got a PhD in bioengineering,
where I spend all my time working on intercortical brain
computer interfaces to control robotic arms.
Basically, any engineer who comes naively into neuroscience
is like, that's totally solvable with a little bit of math.
And then none of the algorithms can quite
perform as well as humans.
And so there's a lot of faith in neuroscience that I've seen,
or that I've seen other people come into,
and they're just like, how are you guys not solving this yet?
It's so simple.
And we invite them in, and then they
don't solve it immediately.
And they start to appreciate that we're trying our best.
Scientists are doing really cool stuff,
but some of these problems are just--
just insane.
If anyone is going to know the language of the brain,
it's an engineer who is trying like every single day
to listen to the brain and then make
a language that a robot could speak from it.
So I asked, what is the language of the brain?
What is it?
How does it talk to itself?
So your neurons in your brain communicate
with electricity, which is super convenient for BCI
researchers, because if it was a chemical,
it would be much harder for us to detect
these single things going on.
If you were using a multimeter, you'd
have kind of the same thing, where you just
want to touch a circuit and see what the voltage is.
Is that not crazy?
In the future documentary, The Matrix,
Morpheus turns a human into a battery.
While, yes, we do have electricity,
we don't have enough electricity to actually power anything.
That was cool sci-fi, but just the fi part.
We generate electricity in the form of ions.
They're not electrons like the plug in your wall.
Instead, it's charged particles or charged molecules
moving through your body.
So sodium, or potassium, calcium, or magnesium--
and each element has an electric charge.
For example, sodium is positive.
So a neuron gets an electrical input
by ions moving from cell to cell.
Proteins on the cell membrane act like bouncers.
So they might let in a sodium ion,
which means the cell creates a positive charge within itself.
The movement of ions from cell one to cell two
moves the charge through our brains,
and it happens really fast--
really fast.
And it's easy to think that's how computers work, right?
You switch on and off.
You have no charge, then you have a charge,
but that's not quite it.
So a computer operates on a binary system.
So it has zeros and ones, and the brain--
some of the neurons operate like that.
Some of them are on or off, but then there
are also neurons that use a gradient for intensity.
So for example, when you sense pain, it's not just,
is there pain, or is there no pain?
But you can sense a different gradient of pain,
like, oh, it kind of stings to, oh my god!
I'm going to die!
So there's all these different gradients
for that type of sensation, but there's a threshold
for these types of sensations.
And so your sensation will be no pain up until a certain point,
and then after which there is dedicated neurons that
say, OK, now we're on, but then beyond that,
there could be a little bit on and a lot on.
And so there's different binary systems,
but also gradiented systems within the brain.
But let's say zero is inhibitory,
and one is excitatory.
Excite-- yeah, I can't say that word.
But so inhibitory is like, nope, don't turn [INAUDIBLE] Stay.
Off and then excitatory is like, OK, your turn
to pass on this information.
Take it.
So that's kind of--
But what is in the information packet?
Is it--
We don't know.
Yeah, that's what I thought.
We don't know.
That's crazy.
Oh, that's so weird.
They're just sending GIFs.
That's what I feel.
They're sending GIFs to each other.
It is sending memes.
So when a cell wants to communicate something
to another cell, it fires an action potential.
We call it a spike.
It's very quick.
It's little bursts-- one millisecond.
Pew, sends the data.
And we are able to record that because it's
a really high amplitude relative to the rest of the thing,
and by high amplitude I mean like maybe 100
microvolts peak to peak.
It's a little guy, but we have 200 things monitoring
basically 200 individual populations of cells,
so you might get up to three or four
cells on a single electrode tip.
And so the arrays are 4 millimeters by 4 millimeters.
They fit on [INAUDIBLE] a dime, but then the array length
is 1.5 millimeters.
So they go 1.5 millimeters into your brain,
which is just enough to go right to where your motor cortex--
the areas that, you know, protect your spinal cord
are sending out commands saying move.
Move this way.
Move that way.
And so we eaves drop on these neurons,
and so people are typically spinal cord injured.
So their brain is perfectly healthy,
but they've lost the link between their brain
and their limbs.
And so instead of restoring that link, because we don't know how
yet, we can record the same activity
that would normally be happening if you thought move my arm.
And instead, we just kind of redirect that to a computer--
the computer processes it, decodes it, and then
we send that to a [INAUDIBLE].
But the thing is we don't know what processing
goes through that neuron's mind for it
to know whether or not it needs to inhibit or excite.
So that's-- that's kind of the mystery that we're trying
to figure out.
So we're all studying different aspects of the same thing,
and it's so crazy that you can break down
the understanding of how our brain functions
into different things.
It's so crazy.
I read things and literature all the time.
I'm like, how did you think about that?
How did you even think to look at that.
That's insane.
Oh my god, this is so crazy.
And it's not over yet.
One way to think about the language of the brain
is to think neuron on, neuron off.
But it's really more neuron signaling, neuron inhibiting
other neurons from signaling.
So it's not really analogous at all.
In a bit in a computer, one bit, zero or one,
electrons flip the switch to on or off, and that's it.
But inside of our heads, each neuron
is kind of like its own little computer.
The neuron might get lots of different input,
and then at some point, pow, output.
So our brain is constantly on and taking in things all
the time from different senses.
It's like, OK, so each neuron gets several inputs.
How does it know what is what?
And how does it know which receiving neuron needs what?
So that's the part that we're trying
to understand in neural coding.
And it still blows my mind to think about it
that these little tiny cells receive so much input,
and then they have to figure it out in microseconds,
and pass it on to the next one.
And I'm a human being, and I can't even
deal with too many things at once.
That said, we can still learn what the brain is doing,
even if we can't exactly digitally decode it.
And we do that by looking at lots of different brains.
We use the brain computer interface,
as I mentioned earlier--
EEGs, fMRIs, physical electrodes-- and we
learn the brain's analog patterns.
When the brain is doing this, we know that means this.
And with training, people can control some pretty
extraordinary things.
The way we decode what's happening in the brain
requires us to know what we're looking for.
Movement, for example-- there are specific areas
of the brain that are dedicated to movement,
and so when you think, oh, I'm going
to flex my bicep a couple times, a computer
can pick up that signature.
And then you would basically tell the computer
what thinking of flexing a bicep looks
like in the electromagnetic field.
From there, the computer can then
tell a prosthetic arm or something
to flex a certain muscle.
Basically, there was this linear map
between what your neurons are doing
and the direction your arm is moving.
And on top of that there's a linear approximation
for how you're moving in space-- how
you're moving your arm in space and what
your neurons are doing.
So with those two things, you can put it together,
and you can start--
you can record from a monkey's brain
while he's alive, and moving around, and not paralyzed.
And then you try to see how well you can guess where his arm is
based on his neural activity.
This kind of stuff was done in the '80s,
and it was the foundation that all BCI is based on.
Did you catch that, by the way?
The brain is sending linear velocity signals--
linear.
When you want to move your arm to the right,
the brain is literally mapping a signal that goes to the right.
That is so crazy, right?
So let's say that the brain wants to draw a circle.
Got my little handy dandy pencil.
I'll draw my circle.
Not the best circle.
Let me try that again.
That's not bad.
That's not bad, actually.
The brain has to measure velocity,
and angle, and pressure, all in three dimensions.
We know how to do this because we monitored thousands
of different animal trials.
Using monkeys where they literally drew circles
while scanning their brains, scientists
realized which neurons work in the monkey brains for this.
Making and arm move-- that's easy, but little finger
movements, that's hard.
There is a lot to learn when you're
drawing the perfect circle.
And I am terrible at drawing.
I draw occasionally for this show,
trying to illustrate something.
And I was trying to get better at drawing.
So I watching this Skillshare class by Shantell Martin.
I mean, we're all at home right now,
and so I also just needed a hobby.
So why not learning to draw?
Shantell said to try drawing rapid fire,
or maybe try it with music, or try standing up.
And all of this was so fun and so weird,
and I did actually get better at illustrating things,
because I started to practice more, and it was kind of fun.
If you want to learn something new,
you should check out Skillshare.
It's an online community of creative learners
who teach each other new skills, like graphic design or habit
formation, learning to draw, art, marketing, business,
web development, video or music production,
even tips on how to craft or pick out art
to hang on your walls.
It's really awesome.
Just head over there, and you're going
to get two months free with this link.
I'm always online searching for how-tos
and trying to discover new ways to do things for video editing
or DIY stuff.
But whereas before I went and just randomly searched,
now I go to Skillshare first.
Joint Skillshare with my link, and again, you're
going to get two months of premium membership for free,
and you can try all sorts of new things,
and learn something new.
And after that, it's only $10 a month to keep it rolling.
Skillshare love's creative and curious people
who are lifelong learners, and I was
like, that sounds like my people.
So thanks.
But let's go back to the brain.
So the language of the brain depends
on a lot of little things.
Luckily, it seems to be universal.
There are different animal brains
that generally work in the same fashion, which
means the language of the brain is decodable,
but it's not as simple as a computer.
It's not binary.
It's not on and off.
I mean, we haven't even really talked
about how it's affected by emotions,
or if we try to do multiple tasks at a time.
Let me go back to Morpheus and what he said.
Remember the battery thing?
Here's one that's a little more accurate.
If real is what you can feel, and smell, taste, and see,
then real is simply electrical signals
interpreted by your brain.
That is the brain's language, and we're
trying to listen to it through the wall of our neighbor's
apartment, and trying to figure out
exactly what they're saying.
But more on that next time.
Thanks to Sharlene, Lietsel, and Jessica for their help so far.
We'll see you on Friday for episode three of five
in this series.
If you you any questions or want to talk
about what you learned today, make sure
you head down into the comments.
I am Trace, and I will see you in the future.