Latest / Talking Science / The Human Brain – Three Pounds of Mystery

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The Human Brain – Three Pounds of Mystery · Talking Science

0:00
Right now, as you are sitting there listening
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to this, your brain is quietly burning enough
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electricity to power a small LED bulb. Which,
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I mean, that might not sound like a lot at first.
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No, it doesn't. Until you realize that this quiet
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electrical hum is happening constantly, whether
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you're wrestling with a complex math problem
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or folding a mountain of laundry or just... you
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know staring blankly out a window right it never
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stops it's relentless exactly you are casually
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carrying around the absolute most complex object
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in the known universe sitting right behind your
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eyes and it is relentlessly hungry and that hunger
0:36
is exactly what we're going to get into today
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yeah so today our mission for this deep dive
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is to completely demystify this astonishing structure
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for you we're going to take our time assume absolutely
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zero prior science background and just strip
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away all that intimidating jargon because the
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jargon really gets in the way of the sheer awe
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of it. It really does. We just want to explore
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the physical reality of the human brain. And
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exploring that physical reality requires us to
1:01
immediately abandon, well, a lot of our preconceived
1:05
notions about what a powerful machine actually
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looks like. Oh, totally. Because when you look
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at the raw physical statistics of the human brain,
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they almost seem like a biological joke. A joke?
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How so? Well, we're talking about... Roughly
1:18
one and a half kilograms of soft tissue. It essentially
1:20
has the consistency of firm jelly. Firm jelly.
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That's kind of unsettling to think about. Right.
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If you were to hold a brain in your hands, it
1:30
would barely hold its own shape without the support
1:32
of the skull and that cerebrocanal fluid it floats
1:35
in. It's incredibly delicate. But there's a huge
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paradox here. The ultimate paradox. That delicate,
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firm jelly makes up only about 2 % of your total
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body mass. Just 2 %? 2%. But it demands and consumes
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a staggering 20 % of your body's entire energy
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supply every single day. That is just a staggering
1:54
disproportion. I mean, imagine having this massive
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sprawling house. And in the front hallway, you
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have one single, tiny, unassuming smart bulb.
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Okay, I like this analogy. Right, so you have
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this little bulb, but when the electricity bill
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comes at the end of the month, you find out that
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this one little bulb is responsible for a fifth
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of your entire power consumption for the whole
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house. You would definitely think there was a
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short circuit. Yeah, or you'd demand to know
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what on earth that bulb is actually doing to
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draw so much power. And that right there is the
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ultimate biological mystery. Because if we frame
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this through the lens of evolutionary biology,
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We know that evolution is an incredibly ruthless
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accountant. It doesn't tolerate waste. Absolutely
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uncompromising. It does not tolerate waste, especially
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not energy waste. Historically, energy meant
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finding calories in a very dangerous, highly
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competitive environment. So you couldn't just
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waste it on something that wasn't pulling its
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weight. Exactly. Evolution would never, ever
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maintain an organ. That is this wildly metabolically
2:57
expensive if it weren't doing something extraordinary
3:00
to ensure our survival. The energy cost is astronomical.
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Yet, despite paying that caloric tax every second
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of every day of our lives, humanity as a whole
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understands this jelly less well than we understand,
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like the life cycles of stars burning billions
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of light years away. It's true. We can map the
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cosmic microwave background of the universe,
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but our own internal wiring remains this massive
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uncharted frontier. So to understand how this
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energy hungry jelly actually works, we have to
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start by zooming way, way in. We can't just look
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at the whole 1 .5 kilogram mass at once. Right.
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You have to look at the microworkers. The fundamental
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building blocks doing all the heavy lifting.
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We're talking about the neurons. And right off
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the bat, there is a massive historical correction
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that we need to address about just how many of
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these cells we actually possess. Yes. This is
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a big one. For a very long time, if you opened
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almost any biology textbook or read a popular
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science article, you'd see the number 100 billion
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repeated over and over again. 100 billion neurons.
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Right. It became this universally accepted fact.
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It was repeated so often that literally no one
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questioned it. But it was wrong. It was, in reality,
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just a very rough, rounded estimate. It wasn't
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until a highly careful, rigorous count was conducted
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in 2009 using a completely new method where they
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basically dissolved brain tissue into a uniform
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suspension, like a soup, to accurately count
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the nuclei that the number was formally revised
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downward. Okay, so what's the actual number?
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The actual figure is roughly 86 billion neurons.
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Okay, I have to stop you there because my immediate
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instinct is to look at those two numbers, 86
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billion versus 100 billion, and wonder why a
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14 billion difference actually matters. It sounds
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like splitting hairs at that scale. Exactly.
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When you're dealing with numbers that astronomical,
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isn't it all just essentially billions and billions?
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Does it really change our fundamental understanding?
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It changes it profoundly. Because in science,
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precision is everything. 14 billion is not just
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a rounding error that we can just sort of wave
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away. Why not? To put that 14 billion difference
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into physical perspective, 14 billion neurons
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is roughly the entire brain capacity of a baboon.
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Wait, really? A whole baboon? A whole baboon.
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So for decades, we were casually tossing an entire
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extra primate brain into our estimates of human
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computation. Wow. We were literally hiding a
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primate in the mat. Exactly. And the reason this
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matters so much isn't just about getting the
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trivia right. It highlights... How much of foundational
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brain science has historically been built on
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casual assumptions? Like we just accepted a nice
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round number because it looked clean on a chalkboard.
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Exactly. It's a stark reminder that we need rigorous
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checking in this field. That is a phenomenal
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perspective. So we have these 86 billion neurons,
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but they aren't even sitting in there alone,
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right? There is another type of cell that makes
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up roughly the other half of the brain's mass.
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And they rarely get the spotlight. Right. We
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have a comparable number of these. Roughly another
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86 billion called glial cells. Glial cells are
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truly the unsung heroes of the nervous system.
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And historically, there was a massive paradigm
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shift in how we understood them, too. I read
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that the name itself, glia, actually comes from
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the Greek word for glue. It does. And for decades,
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neuroscientists dismissed them as mere structural
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support tissue. Like they were just holding the
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important stuff together. Right. The prevailing
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thought was that they were essentially biological
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bubble wrap. They existed purely to keep the
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important electrically active neurons physically
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insulated and locked into place. Just like packing
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peanuts for the brain. Entirely passive. That
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was the assumption. But today, that view has
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been completely overturned. What do they actually
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do then? We now understand that glial cells are
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incredibly dynamic, active players in the brain's
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environment. They don't just sit there. They
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are actively involved in chemical signaling.
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Oh, so they communicate too. Yes, and we manage
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the brain's highly specialized immune function,
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hunting down pathogens and clearing away cellular
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debris. That's a lot more than bubble wrap. And
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crucially, they actively regulate the connections
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between neurons, determining which pathways get
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strengthened and which get pruned away. The brain
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simply could not function without them. They
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aren't the scaffolding. They are an integral
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part of the active machinery. So we have this
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unimaginably dense active forest of neurons and
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glial cells. Let's look closer at the main characters,
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the neurons themselves. Good idea. The anatomy
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of a neuron can be broken down into three main
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functional parts. And it's actually a beautifully
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intuitive structure to visualize. First, you
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have the dendrites. Right, the receivers. Yeah.
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These are highly branched structures that radiate
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out to receive signals from other neurons. I
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sort of picture them like the sprawling, intricate
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branches. of a mature oak tree just reaching
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out in every direction, waiting to catch incoming
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signals. That is an excellent visualization.
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They are the chemical and electrical receiver,
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the input zone, basically. Then moving down from
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those branching receivers, you have the cell
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body, also known as the soma. Right, the soma.
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And this is the central hub of the cell where
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the nucleus lives. It acts as the integrator.
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It takes all those thousands of inputs caught
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by the dendrite branches and makes sense of the
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noise. It does all the math. Exactly. And finally,
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you have the axon. This is a single long cable
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that carries the cell's output signal away to
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its next destination. And the axon brings us
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to one of the most counterintuitive physical
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facts about the human body. What is? Well, when
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we think of a cell, we are conditioned to think
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of something strictly microscopic. we picture
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something we need a powerful lens to see. Right,
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like an amoeba or a red blood cell. Exactly.
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And the cell body of a neuron is indeed microscopic,
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but that single output cable, the axon, some
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of those single cellular extensions run the better
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part of a meter in length. Wait, I really want
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to make sure I'm visualizing this correctly.
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You were saying a single microscopic cell body
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has an attached cable that is like three feet
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long. That is exactly right. There are motor
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neurons where the microscopic cell body sits
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safely tucked inside. the base of your spinal
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cord, but its axon stretches all the way down
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your leg, terminating at the muscles in your
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big toe. That is wild. It's a single, continuous,
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unbroken cellular tube. To put the scale into
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everyday terms, if you were to scale the microscopic
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cell body up to the size of a standard tennis
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ball, that single axon would be a garden hose
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stretching for over a mile. A mile -long garden
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hose. completely defies our normal sense of scale.
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It really does. And it immediately raises a huge
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mechanical problem in my mind. We have this physical
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hardware. We have the 86 billion neurons, the
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tree branches receiving the cell body processing
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and these mile long biological hoses sending
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outputs. Right. But how does a signal actually
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travel across those massive distances without
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just, you know, fading into nothing? How do they
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talk to each other? The brain, as we know, uses
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a bilingual system, right? Yes. It communicates
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using both electrical and chemical languages.
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So how does the... Electrical part work. The
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distinction between those two languages is vital
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to understanding everything the brain does. So
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let's start with what happens inside a single
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neuron. Within the neuron itself, traveling from
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the cell body all the way down that incredibly
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long axon cable, the communication is purely
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electrical. Purely electrical. Yes. It is a wave
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of voltage change that sweeps along the membrane.
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In neuroscience, this event is called an action
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potential. Now, I want to clarify how this electrical
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wave actually works, because it's not like electricity
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flowing through a copper wire in a wall, right?
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It follows a very specific rule. It does. It's
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an all or nothing event. Right. All or nothing.
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You often hear it compared to a text message
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like, you can't half send a text. It either sends
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or it doesn't. But physically, I think a better
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way to visualize the action potential is like
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a stadium wave of falling dominoes. A line of
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dominoes is a brilliant way to picture it because
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it perfectly captures the mechanism of how the
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signal actually propagates. Right, because when
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you tip the first domino, it falls and hits the
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second, which hits the third, all the way down
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the line. You can't have a weak domino fall.
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Exactly. Once the first one tips past a certain
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angle, the entire line is going down with the
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exact same momentum. the wave travels all the
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way to the end of the axon without losing any
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strength because each segment of the axon is
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actively generating the next step of the wave.
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And that domino analogy brings us right back
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to our opening paradox about the brain's massive
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20 % energy consumption. Oh, how so? Well, think
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about a mile -long line of dominoes. Once they
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have all fallen down, they cannot carry another
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signal until someone painstakingly walks down
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the entire line and stands every single domino
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back up one by one. Oh, wow. I didn't think about
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the reset. Right. In the neuron, this standing
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the dominoes back up involves molecular pumps
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that physically push sodium and potassium ions
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back across the cell membrane against their natural
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gradients. And I'm guessing that takes a lot
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of energy. Immense amounts of biological energy.
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That is where a massive chunk of your calories
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goes. Just constantly resetting the electrical
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dominoes so you are ready to think your next
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thought. That makes so much sense. But wait.
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if the electrical wave is a falling domino if
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the signal strength is always exactly the same
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and all or nothing pulse how does my brain know
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the difference between say a gentle breeze brushing
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against my arm and someone violently slamming
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a door on my finger that's a great question because
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if the single pulse is identical where is the
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information about intensity coming from well
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If the information cannot be carried in the strength
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or the size of the electrical signal, it must
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be encoded in the timing and the frequency. The
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frequency? Yes. The brain encodes intensity through
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Winnem. A gentle breeze might cause that sensory
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neuron to fire an action potential to knock down
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its line of dominoes maybe five times a second.
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Pop, pop, pop. Okay, kind of a slow rhythm. Right.
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But a slammed finger will cause that exact same
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neuron to frantically fire 50 or 100 times a
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second. Pop, pop, pop, pop. The individual electrical
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spikes are identical in size, but the rapid -fire
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frequency instantly tells the central nervous
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system that this is an intense, urgent stimulus.
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So the frequency is the message. But that electrical
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domino wave only gets you so far, right? It only
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gets you to the very end of the axon. Exactly.
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And here is the twist that makes the brain bilingual.
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The axons do not actually touch their intended
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targets. They don't touch at all. No, they do
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not. And this is a foundational concept. At the
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very end of that long cable, There is a microscopic
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physical gap between the sending cell and the
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receiving cell. A physical break. Yes. It is
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incredibly small, only nanometers wide, but it
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is an absolute physical break in the circuit.
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This gap is called a synapse. So the electrical
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stadium wave hits a literal cliff. It reaches
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the end of the axon and has nowhere to go. Right.
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And electricity in the biological environment
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of the brain cannot simply arc across empty space
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like a spark plug in a car engine. The electrical
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signal hits a dead end. So how does it cross?
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To bridge that gap, the electrical signal forces
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a profound transformation. It triggers a chemical
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process. Ah, here's the second language. Exactly.
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When the action potential arrives at the end
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of the axon, the change in voltage forces tiny
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cellular packages to fuse with the membrane and
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dump millions of molecules into the gap. These
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molecules are called neurotransmitters. Okay,
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these are the chemicals we hear about in popular
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culture all the time, right? Like glutamate,
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GABA, dopamine, and serotonin. Those are the
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ones. These are the chemical messengers. I kind
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of picture them as... Broadly speaking, they
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fall into two camps. Some neurotransmitters,
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like glutamate, are excitatory. When they cross
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the gap and bind to the receiving cell, they
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essentially vote yes. They bring the receiving
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cell closer to firing its own action potential.
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And the other camp? Others, like GABA, are inhibitory.
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When they bind, they vote no. They actively suppress
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the receiving cell, making it much harder for
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it to fire. So it's not a relay race at all.
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It's an insanely complex summation game. It's
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like a microscopic voting system happening in
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real time. It is exactly a voting system. Right.
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Remember how we said the dendrites are like sprawling
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tree branches? Yeah. A single neuron's branches
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might be covered in 10 ,000 individual synapses.
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It is receiving chemical inputs from 10 ,000
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other cells simultaneously. That is just mind
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-boggling. It is. Some are aggressively shouting
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fire, dumping excitatory chemicals. Others are
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whispering stay quiet, dumping inhibitory chemicals.
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And the receiving cell just has to figure out
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who wins. Exactly. The receiving cell body acts
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as a continuous biological calculator. It sums
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up all the yes votes and all the no votes millisecond
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by millisecond. Only when the excitatory signals
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overpower the inhibitory signals by a specific
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critical threshold does that neuron generate
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its own electrical domino wave and send the message
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down its own axon. When you step back and look
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at the sheer scale of this voting system, it
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is dizzying. We already established there are
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86 billion neurons. But the true source of your
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intelligence, your memories, and your personality
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isn't that raw cell count, is it? No, it's not.
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It's the number of those tiny gaps. It is the
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synapses. The connection density is the true
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engine of human capability. The estimates suggest
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that the human brain contains roughly 100 trillion
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synapses. 100 trillion. 100 trillion chemical
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voting stations constantly calculating, constantly
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summing up inputs. It's a number so large it
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completely defies human comprehension. I can't
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even picture a number that big. Well, if you
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tried to count to 100 trillion. Simply saying
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one number out loud every second, it would take
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you over three million years to finish. Wow.
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And it's not even a static structure, which brings
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us perfectly to a concept that is so crucial
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to understand neuroplasticity. A very important
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concept. Because with 100 trillion connections
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constantly firing, voting, and summing, you might
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picture the brain as this fixed, rigid, unimaginably
17:06
complex circuit board. Right, like a computer.
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like the motherboard of a supercomputer that
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is soldered into place at birth and just runs
17:12
its programs. But the brain is entirely different.
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It is constantly physically changing its own
17:18
hardware based on those exact chemical and electrical
17:21
signals. And we really need to define neuroplasticity
17:24
carefully here because it is a term that has
17:26
been thoroughly distorted by popular culture.
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It really has. It has become the ultimate buzzword
17:31
for the self -improvement and wellness industries.
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You see it on book covers, seminars, and life
17:36
coaching blogs all the time. Oh, everywhere.
17:38
Rewire your brain for success. Exactly. Rewire
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your brain for immense wealth or unlock the neuroplasticity
17:46
of eternal happiness. Let's strip away all of
17:49
that marketing hype. What is the actual physical
17:52
basis of neuroplasticity in the firm jelly of
17:55
the brain? In strict physical terms, plasticity
17:58
simply means the brain alters its structural
18:00
connections in response to experience and use.
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So the connections physically change. Yes. Remember
18:06
those synapses, the tiny gaps where the chemical
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voting happens? Yeah. They're not fixed bridges.
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When two connected neurons are repeatedly activated
18:14
together. When the first one fires and successfully
18:16
contributes to making the second one fire, the
18:19
physical and chemical connection between them
18:21
strengthens. The famous phrase is, neurons that
18:23
fire together, wire together. Exactly. On a physical
18:26
level, the sending cell might start releasing
18:28
more neurotransmitter fairies. The receiving
18:31
cell might build more receptor docks to catch
18:32
them. The actual physical shape of the dendrite
18:35
can change, becoming thicker and more robust.
18:37
So the voting power of that specific connection
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increases. Precisely. Conversely, if a connection
18:43
is ignored or rarely used, that synapse weakens
18:46
and can eventually be pruned away entirely, sometimes
18:49
completely new synapses form. So neuroplasticity
18:53
is simply the physical manifestation of learning.
18:56
That is precisely what it is. Everything you
18:58
know right now, every childhood memory you hold,
19:00
every physical skill you have mastered is encoded
19:03
not as... data on a hard drive, but as a specific
19:07
physical pattern of connection strengths across
19:09
millions of synapses. But because of that self
19:12
-help angle I mentioned earlier, I have to ask,
19:14
does this mean there are no limits? Limits to
19:17
plasticity. Yeah, like, can I just practice positive
19:20
thinking, do some brain puzzles, and magically
19:23
rewire my entire brain to become a concert pianist
19:26
and a mathematical genius in my 40s? This is
19:28
where we have to firmly debunk the hype. Plasticity
19:31
does not mean the brain is infinitely malleable.
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So it's not just wet clay. It is not wet clay
19:36
that you can mold into whatever shape you desire
19:38
simply through willpower. You cannot decide to
19:42
relocate your visual processing center to your
19:44
frontal lobe. You cannot fundamentally alter
19:47
your baseline temperament just by wishing for
19:49
a rewire. So it's a biological mechanism, not
19:52
magic. It is heavily bounded. Plasticity is a
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very real, measurable physical process, but it
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is slow, highly energetically demanding, and
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bounded by biology. Furthermore, the degree of
20:04
plasticity changes drastically over your lifespan.
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Meaning it's different for kids and adults. Completely
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different. The brain's ability to structurally
20:13
change is at its absolute wild peak in childhood.
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Children go through what developmental biologists
20:19
call critical periods. What are those? They're
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wide open windows of time where the brain is
20:23
incredibly receptive to profound structural change
20:26
based on the environment. This is why a young
20:28
child can absorb a complex new language with
20:31
seemingly zero effort, perfectly mastering the
20:33
phonemes, while an adult can study the same language
20:36
for 10 years and still struggle with a thick
20:38
accent. In adulthood, the biological window closes.
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The brain becomes much more constrained. You
20:45
can still learn, obviously, but the physical
20:47
changes are slower, require vastly more repetition,
20:50
and are far more localized. And there is a darker
20:53
side to neuroplasticity that rarely makes it
20:55
onto the self -help book covers. We are heavily
20:59
conditioned to assume that plasticity is automatically
21:01
a good thing. Yes, that's a huge misconception.
21:04
It implies personal growth, resilience, and learning.
21:07
But the reality is much more complicated. We
21:10
must remember that biology is entirely neutral.
21:14
Plasticity is not inherently good or bad. It
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is simply the brain efficiently learning and
21:18
solidifying a pattern. It does not possess a
21:21
moral compass to judge whether that learned pattern
21:24
is ultimately beneficial to your life. What does
21:27
that look like in practice? When is it harmful?
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Some of the most profound examples of neuroplasticity
21:32
in human behavior are addiction and chronic pain.
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Addiction is a form of neuroplasticity. Oh, absolutely.
21:38
In the case of severe chemical addiction. The
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brain is ruthlessly and efficiently rewiring
21:43
its reward pathways. It is strengthening the
21:46
synapses that prioritize the seeking and consumption
21:49
of a specific chemical substance and simultaneously
21:53
weakening the neural networks associated with
21:56
other rewards, like social connection or long
21:58
-term goals. Wow. So the brain is learning exceptionally
22:01
well. It's just learning a pattern that destroys
22:03
the organism. And chronic pain works similarly.
22:06
Very similarly. In many cases of chronic pain,
22:09
the initial tissue damage, say a lower back injury,
22:12
has completely healed on a structural level.
22:15
The discs and muscles are fine. But it still
22:17
hurts. Exactly. Because during the injury, the
22:20
nervous system repeatedly fired pain signals.
22:23
Through neuroplasticity, the synapses in the
22:25
pain pathways strengthened. They became incredibly
22:27
efficient at firing. The nervous system essentially
22:30
learned to amplify pain signals, and now it continues
22:33
to fire them loudly long after the physical threat
22:35
is gone. That is neuroplasticity in action. It
22:38
is highly efficient biological learning, but
22:40
it is deeply harmful to the individual. That
22:43
completely reframes the concept for me. It is
22:45
just a mechanism of efficiency, for better or
22:47
worse. Exactly. So we know how the connections
22:50
change, and we know they form these massive networks
22:53
of 100 trillion synapses. But where are these
22:57
specific connections actually clustered? The
23:00
brain isn't just a homogenous bowl of jelly.
23:03
It has specialized geography. It's highly organized.
23:07
Let's take a tour of this real estate, working
23:09
our way from the bottom up. Geography is crucial,
23:12
although we must always caveat that functions
23:14
are highly interconnected. But broadly speaking,
23:17
yes, there are distinct neighborhoods. Starting
23:19
at the bottom. If we start right at the base
23:21
of the skull, where the spinal cord enters the
23:24
head, we find the brainstem. This is essentially
23:27
the brain's survival mode. This region handles
23:30
the functions that you really, really do not
23:32
want to be consciously responsible for managing.
23:34
You definitely don't want to have to think about
23:36
them. Right. It regulates your breathing rate,
23:38
your heartbeat, your blood pressure, and your
23:41
fundamental sleep -wake cycles. It manages the
23:43
absolute non -negotiable life support systems.
23:47
Because these functions are so utterly essential
23:50
for moment -to -moment survival, the brainstem
23:53
is deeply protected. But it also means that physical
23:56
damage to the brainstem, whether from a targeted
23:58
stroke, a tumor, or traumatic swelling, is almost
24:02
universally fatal. Because it just stops the
24:05
engine. Exactly. If the brainstem goes offline,
24:07
the basic rhythmic pacing that keeps the heart
24:10
beating and the lens expanding immediately ceases.
24:13
You can survive massive damage to higher cortical
24:16
areas and remain alive. But the brainstem is
24:19
the biological foundation. Moving just behind
24:22
the brainstem, tucked underneath the main bulk
24:24
of the brain at the back of your head, we have
24:26
the cerebellum. And the statistics regarding
24:28
the cerebellum are genuinely astounding. They
24:31
really are. It is a relatively small volume of
24:33
tissue. It looks like a little cauliflower hanging
24:35
off the back. But it contains more than half
24:37
of all the brain's 86 billion neurons. The cellular
24:41
density of the cerebellum is unlike anywhere
24:43
else in the nervous system. The neurons there
24:45
are packed incredibly tightly. What does it do
24:47
with all those neurons? Functionally. The cerebellum
24:51
is the supreme motor engine of the brain. It
24:54
does not initiate the decision to move that happens
24:56
higher up, but it coordinates the movement. It
24:59
manages balance, posture, and crucially, it handles
25:04
the microscopic precision of timing. Is this
25:06
why physical skills eventually become automatic?
25:09
Like when you first sit down to learn a chord
25:11
on the guitar, it is excruciating. Oh, totally.
25:14
You are consciously commanding every single finger.
25:17
Right. You're commanding every finger to move
25:19
to a specific string, checking your hand visually,
25:21
adjusting the pressure. But 10 years later, you
25:24
don't even look at your hand. You just think
25:26
G chord and your fingers instantly fall into
25:28
the perfect shape. That is exactly the cerebellum
25:31
at work. It takes those conscious, clunky, poorly
25:33
timed motor commands from the cortex and through
25:36
millions of repetitions, refines them. It encodes
25:39
the timing. It encodes the perfect timing and
25:42
sequence of muscle contractions into its densely
25:44
packed networks. It allows you to execute highly
25:48
complex physical actions, whether it's playing
25:50
a guitar, typing on a keyboard, or just walking
25:53
down a flight of stairs while drinking coffee,
25:54
without dedicating conscious computational power
25:57
to the mechanics of the movement. Moving deeper
26:00
inside the center of the brain, nestled above
26:03
the brainstem, we find a collection of areas
26:05
broadly referred to as the limbic structures.
26:08
The emotional center, broadly speaking. Right.
26:11
This is generally understood as the neighborhood
26:13
where memory and emotion live. There are two
26:17
major players here that we should define. The
26:20
hippocampus. and the amygdala. Let's start with
26:23
the hippocampus. It's an elegant, seahorse -shaped
26:26
structure that is absolutely essential for the
26:28
formation and consolidation of new, long -term
26:31
memories. Does it store the memories like a hard
26:33
drive? No, it doesn't permanently store all your
26:35
memories, but it acts as the necessary gateway.
26:38
If your hippocampus is severely damaged, as was
26:41
seen in famous neurological case studies, you
26:44
would retain your intelligence, your personality,
26:46
and your older childhood memories. But you couldn't
26:49
make new ones. Exactly. You would be entirely
26:51
unable to form a new conscious memory. You could
26:54
have a conversation, leave the room, walk back
26:56
in five minutes later and have absolutely no
26:58
recollection of the person you just spoke to.
27:01
That's terrifying. And sitting right adjacent
27:03
to the hippocampus is the amygdala, which is
27:06
famous for its role in emotion. Specifically,
27:10
the amygdala is heavily involved in processing
27:12
emotional salience, which often manifests as
27:15
fear or threat detection. Like an alarm system.
27:18
It acts as an incredibly rapid, deeply ingrained
27:21
biological alarm system. It is constantly scanning
27:24
sensory input, flagging things in the environment
27:27
that are emotionally significant or potentially
27:29
dangerous, and triggering the physiological changes,
27:32
like a racing heart or sweating, before your
27:34
conscious brain even fully recognizes what the
27:36
threat is. And finally, we move outward to the
27:39
wrapper, the part everyone visualizes when they
27:42
think of a brain floating in a jar, the cerebral
27:44
cortex. The wrinkled part. Yes, it is this deeply
27:48
wrinkled, folded outer layer of tissue. I think
27:51
everyone at some point has wondered why it looks
27:53
so crumpled, like a walnut. And the physical
27:56
explanation for this is just a brilliant packing
27:59
solution by evolution. It is a remarkable piece
28:02
of biological engineering. The cortex is the
28:05
seat of our highest level processing. It is where
28:08
complex perception, abstract thought, and conscious
28:11
awareness seem to be anchored. So you want a
28:13
lot of it. Right. To increase an organism's computational
28:17
power, evolution needed to add more cortical
28:20
tissue. More tissue means more neurons and more
28:22
synapses. However, hominids ran into a hard biological
28:26
constraint. The size of the human skull. Exactly.
28:29
A skull can only get so big before passing through
28:31
the birth canal becomes a biological impossibility.
28:34
So you have a fixed, hard, spherical container,
28:36
but you need to fit a massive sheet of tissue
28:38
inside of it. How do you maximize surface area
28:41
within a restricted volume? You crumple it up.
28:43
It is exactly like trying to shove a massive
28:46
winter sleeping bag into a tiny nylon stuffed
28:49
sack. That's a perfect analogy. You can't just
28:52
lay it flat. You have to aggressively fold it
28:54
and compress it, creating all these deep grooves
28:57
and ridges just to maximize the surface area
28:59
that fits inside the limited space. That is precisely
29:02
what the wrinkles, the sulci and gyri of the
29:04
brain achieve. If you were to somehow extract
29:07
the human cerebral cortex and iron it out completely
29:11
flat on a table, it would be roughly the size
29:14
of an extra large pizza. Or, you know, a large
29:16
newspaper page. A pizza inside your skull. All
29:19
those deep wrinkles allow that massive sheet
29:22
of computational power to be jammed into the
29:24
tight confines of the skull. And this vast pizza
29:27
-sized sheet of cortex is broadly divided into
29:30
four major territories, or lobes, on each hemisphere
29:33
of the brain. Let's map these out slowly so you,
29:37
sitting there listening, can actually visualize
29:39
where these functions are sitting on your own
29:41
head. Okay, let's trace it. Starting at the very
29:43
back of the skull, just above the neck, you have
29:45
the occipital lobe. The occipital lobe is almost
29:47
entirely dedicated to the massively complex task
29:50
of vision. It takes the raw electrical signals
29:53
sent from the retinas of your eyes and begins
29:55
the arduous process of decoding them. Like finding
29:58
the edges of objects. Exactly. It finds edges,
30:01
it detects motion, it recognizes color, and it
30:04
begins to assemble those fragments into the cohesive
30:07
visual world you experience. Moving around to
30:10
the sides of the head, roughly in the area right
30:12
around and behind your ears are the temporal
30:15
lobes. The temporal lobes are the primary centers
30:18
for processing auditory information hearing,
30:21
crucially on the left side for most people. This
30:24
is where language comprehension is heavily anchored.
30:27
And they sit right next to the hippocampus, right?
30:29
Yes. The temporal lobe is deeply connected to
30:32
the hippocampus, making it a critical region
30:34
for integrating those auditory and visual inputs
30:37
into complex, retrievable memories. Then, moving
30:40
up to the top of the head, sitting like a crown,
30:42
is the parietal lobe. The parietal lobe is the
30:45
grand integrator of sensory information. It processes
30:48
the sense of touch, temperature, and pain from
30:51
across your body. And spatial awareness, too.
30:53
Yes. Profound spatial awareness. It constantly
30:57
calculates where your limbs are in three -dimensional
30:59
space without you having to look at them, and
31:01
it integrates that map of your body with the
31:03
visual map of the world around you so you can
31:05
navigate without constantly crashing into walls.
31:08
And finally, we reach the massive section occupying
31:11
the entire front of the skull, sitting right
31:13
behind your forehead. The frontal lobe. The frontal
31:17
lobe is arguably the region that makes human
31:19
behavior so distinctively human. It doesn't do
31:21
raw sensory input, right? No. Instead, it is
31:24
the executive command center. It handles complex,
31:27
multi -step planning. It weighs the outcomes
31:30
of difficult decisions. It originates the command
31:32
for voluntary movement. And critically, it governs
31:36
the crucial social skills of impulse control
31:39
and emotional regulation. And the frontal lobe
31:41
deserves a very deep dive here because of its
31:43
unique developmental timeline. We think of organs
31:46
generally growing and maturing together, but
31:48
the frontal lobe is on a massive delay. It is
31:51
the absolute last region of the brain to fully
31:53
biologically mature. How late are we talking?
31:56
Well, while your sensory regions are fully locked
31:58
in very early in life, the physical wiring and
32:01
the insulating myelin sheaths of the frontal
32:03
lobe continue to actively develop and solidify
32:06
well past adolescence, typically continuing into
32:09
a person's mid -20s. Think about the profound
32:12
real -world implications of that biological fact.
32:16
The area of the brain strictly responsible for
32:19
overriding impulses, for foreseeing long -term
32:22
consequences of dangerous actions, and for regulating
32:25
erratic behavior is essentially a construction
32:28
zone with wet paint and scaffolding throughout
32:31
the entirety of the teenage years. It provides
32:34
an enormous amount of biological context for
32:36
adolescent behavior. Teenagers have highly active
32:39
emotional centers. Their amygdalas are firing
32:41
on all cylinders. But the executive braking system
32:44
in the frontal lobe is not yet fully installed.
32:46
And this biological reality has actively changed
32:49
how society operates, specifically within the
32:51
legal system. It has forced a major reckoning
32:54
in juvenile justice, yes. Because if the hardware
32:56
isn't there... Exactly. If the biological hardware
32:59
required for mature impulse control and risk
33:02
assessment is simply not yet physically mature
33:05
in a 19 -year -old, it forces courts and society
33:08
to ask highly difficult questions about ultimate
33:11
culpability. It shifts the conversation toward
33:14
rehabilitation rather than pure punishment for
33:17
offenders under the age of 25 because their brain
33:20
architecture is quite literally not finished
33:22
developing. Precisely. When we talk about the
33:24
frontal lobe and personality, it is impossible
33:26
not to discuss the most famous cautionary tale
33:29
in the history of neuroscience. The story of
33:32
Phineas Gage. Oh, everyone has heard of Phineas
33:35
Gage. It's a story almost everyone has heard
33:36
a version of. The year is 1848. Gage is a young,
33:40
capable railway foreman working in Vermont. He
33:43
is packing explosive powder into a rock hole
33:46
using a heavy iron tamping rod. A very dangerous
33:48
job. Very. A spark ignites the powder prematurely
33:51
and the explosion shoots this massive iron rod
33:54
over a yard long and an inch thick straight up
33:57
through his left cheek behind his eye and violently
33:59
out the top of his skull, completely destroying
34:01
a massive chunk of his frontal lobe. And against
34:04
all medical odds for the 19th century, he survives.
34:07
His survival alone made it a medical marvel.
34:09
But the reason Phineas Gage became a foundational
34:12
pillar in every psychology textbook is the reported
34:15
aftermath of the injury. Right. The classic dramatic
34:18
narrative states that Gage survived. But his
34:21
personality was entirely obliterated. He reportedly
34:23
went from being a polite, hardworking, highly
34:26
respected foreman to a completely impulsive,
34:30
profane, animalistic person who could not hold
34:33
a job or conform to social norms. The famous
34:36
quote from his physician was, Gage was no longer
34:39
Gage. It seemed to perfectly neatly prove that
34:42
the frontal lobe was the sole seat of human morality
34:45
and restraint. But the reality of Phineas Gage
34:48
is vastly more complicated than the textbook
34:50
myth, isn't it? The historical narrative has
34:52
been aggressively streamlined. It is one of the
34:55
clearest examples of scientific folklore in modern
34:57
history. Did Gage suffer catastrophic frontal
34:59
lobe damage? Yes. Did he experience severe behavioral
35:03
changes immediately following the trauma? Absolutely.
35:05
So what's the myth part? Over the ensuing decades,
35:08
textbook authors heavily embellished his symptoms
35:11
to make a cleaner, neater pedagogical point about
35:14
the strict localization of brain function. They
35:17
turn him into a one -dimensional monster. What
35:20
does the actual historical record show about
35:22
the rest of his life? The historical records,
35:24
carefully unearthed later, suggest that Gage
35:27
actually experienced a profound degree of social
35:29
recovery. Later in his life, he traveled to Chile,
35:32
where he successfully held down a demanding job
35:35
driving a stagecoach for years. Driving a stagecoach
35:38
in Chile, that's not a job for someone who is
35:40
completely impulsive. Exactly. Driving a stagecoach
35:43
with a team of horses requires immense multi
35:46
-step planning, sustained focus, complex motor
35:49
control, and the ability to interact with passengers.
35:52
A completely impulsive, uninhibited person could
35:54
never maintain that job. So his remaining brain
35:57
tissue, perhaps utilizing that neuroplasticity
36:00
we discussed earlier, found workarounds. It compensated
36:03
for the damage. That's the most likely explanation.
36:06
The true lesson of Phineas Gage is not that the...
36:09
It manners. The lesson is that the brain is highly
36:14
interconnected, incredibly resilient, and capable
36:17
of profound compensation. It's a warning not
36:20
to oversimplify things. Exactly. we must always
36:23
be wary of incredibly neat, overly dramatic narratives
36:26
that perfectly support a simplified theory. And
36:29
that transition into scientific folklore brings
36:32
us to a vital part of our mission today. Because
36:35
the brain is so wildly complex and because we
36:38
all possess one, misinformation about it is unusually
36:41
persistent. It sticks in the cultural consciousness
36:43
and refuses to die. We need to clear out some
36:46
of these cobwebs. Let's methodically break down
36:48
and bust some of the most persistent brain myths
36:50
that are still actively circulating. I am more
36:53
than happy to do this. The sheer volume of biological
36:56
misinformation in popular culture is staggering.
36:59
Let's start with myth number one, the absolute
37:01
classic undisputed king of brain myths. The 10
37:04
% myth. I cannot even count how many blockbuster
37:06
movie plots rely entirely on this premise. Oh,
37:09
it's so common. The protagonist takes an experimental
37:11
pill or gets exposed to radiation. And suddenly
37:14
the scientist says, we only use 10 % of our brains,
37:17
but he just unlocked the other 90%. And suddenly
37:20
the character has telekinesis, can predict the
37:23
stock market, and learns conversational Mandarin
37:25
in five minutes. It is a fantastic hook for a
37:29
science fiction movie. It taps into our deep
37:31
-seated desire for untapped potential. But biologically,
37:36
physiologically, and neurologically, it is entirely
37:39
unequivocally false. We use 100 % of our brain.
37:43
If someone asked for the concrete proof, how
37:45
do we definitively know there is no hidden 90
37:48
%? We can prove it from multiple independent
37:50
scientific angles. First, we have modern functional
37:53
brain imaging. like fMRI and peace cans. What
37:56
do those show? Whether a subject is performing
37:58
a highly complex calculus equation, reading a
38:00
novel, or simply sleeping soundly, functional
38:02
imaging shows waves of electrical and metabolic
38:04
activity sweeping constantly throughout the entire
38:07
mass of the brain. There are no dark, dormant,
38:10
completely silent biological voids just sitting
38:12
there waiting for a magic pill to activate them.
38:14
And the second angle ties right back to our opening
38:17
discussion about energy, right? Precisely. The
38:19
metabolic cost... absolutely destroys the 10
38:23
% myth. Because it takes 20 % of our calories.
38:25
Right. Maintaining living neural tissue is phenomenally
38:29
expensive. If 90 % of that tissue were completely
38:32
idle, evolution would have ruthlessly eliminated
38:35
it millions of years ago. It would be evolutionary
38:38
suicide for a species to carry around three extra
38:41
pounds of highly demanding calorie -burning tissue
38:44
if it served no active function. That makes total
38:46
sense. Furthermore, in clinical neurology, we
38:49
observe that damage to almost any area of the
38:51
brain, even a tiny stroke in an obscure region,
38:54
results in... some specific measurable deficit.
38:57
There is no spare 90 % that you can comfortably
39:00
damage without consequence. Every region has
39:03
a job. Consider myth number one entirely busted.
39:06
Moving on to myth number two, left brain versus
39:09
right brain personalities. You see this everywhere
39:11
in corporate team building exercises and online
39:14
personality quizzes. Those quizzes are everywhere.
39:17
The premise is that you are either a logical,
39:19
analytical, math -driven, left -brained person,
39:22
or you are a creative, artistic, free -flowing,
39:26
right -brained person. This myth is particularly
39:28
tricky to eradicate because unlike the 10 % myth,
39:31
it is built on a very tiny, actual grain of neuroscientific
39:34
truth. But that truth is then wildly, inappropriately
39:38
extrapolated. What is that initial grain of truth?
39:41
The grain of truth is a concept called lateralization.
39:44
The two hemispheres of the brain do, in fact,
39:46
show some specialization for certain tasks. Like
39:49
what? For example, in the vast majority of the
39:51
population, the heavy lifting for fine language
39:54
processing, grammar, vocabulary, speech production
39:56
is strongly lateralized to the left hemisphere.
39:59
Conversely, certain complex spatial integration
40:02
functions or recognizing faces lean more heavily
40:05
on the processing power of the right hemisphere.
40:07
So if language and math lean left, why does it
40:10
remain a myth that I am a left -brained person?
40:13
Because lateralization of a specific sensory
40:15
or motor function does not translate into a unilateral
40:19
global personality type. There is no such thing
40:22
as a unilaterally logical half of a brain and
40:26
a unilaterally creative half. They don't work
40:28
independently. The brain does not silo complex
40:30
behaviors. Whenever you perform essentially any
40:33
high -level task, whether you are painting a
40:36
landscape, solving a differential equation, or
40:39
trying to understand a sarcastic joke, Both hemispheres
40:43
are lighting up simultaneously and working together
40:45
in constant concert. And they are physically
40:47
connected. They aren't two separate computers.
40:49
Exactly. They are bridged by a massive, dense
40:51
band of white matter called the corpus callosum.
40:54
This structure is composed of roughly 200 million
40:57
individual nerve fibers. 200 million? Yes. It
41:01
acts as a massive biological broadband cable.
41:03
Those 200 million fibers are constantly, frantically
41:06
exchanging billions of bits of information back
41:08
and forth between the atmospheres every second.
41:11
The hemispheres are in constant dialogue. You
41:13
are not a left brain person or a right brain
41:15
person. You are a wholly integrated, whole brained
41:18
person. Yeah. Let's move to myth number three.
41:20
And this one is so deeply embedded in our global
41:24
education system that it feels almost heretical
41:27
to question it. Learning styles. Yes, this is
41:30
a big one. It's the deeply held idea that students
41:33
are inherently either visual learners, auditory
41:35
learners, or kinesthetic learners, and that teachers
41:38
must tailor their instruction to match a student's
41:41
specific style for them to learn effectively.
41:44
This is perhaps one of the most frustrating myths
41:46
for cognitive scientists and educational psychologists
41:49
to combat. It has immense intuitive appeal. It
41:53
feels correct. But is it? When the theory is
41:56
rigorously scientifically tested in controlled
41:59
environments and has been tested repeatedly over
42:01
decades, the core claim consistently fails. Wait,
42:04
I really need to drill down on this because I
42:06
personally feel a strong conviction that I learn
42:08
better when I see a clear diagram rather than
42:10
just listening to a lecture. How can it be a
42:12
myth? The key is distinguishing between a subjective
42:15
preference and a measurable educational outcome.
42:18
People absolutely have strong preferences. Okay,
42:20
so I prefer it. Right. You might deeply prefer
42:23
to look at a diagram. Someone else might prefer
42:25
to listen to an audiobook. But preference is
42:28
not the same as a biological constraint on learning.
42:31
What happens when they test it? When researchers
42:34
actually run the empirical experiments, when
42:37
they take a group of self -identified visual
42:39
learners and randomly assign half of them to
42:42
learn a subject via text and the other half to
42:44
learn via diagrams, the outcomes, the actual
42:48
test scores assessing their comprehension, do
42:50
not show a statistically significant difference
42:53
based on matching their preferred style. So matching
42:57
the teaching method to the self -reported style
42:59
doesn't actually boost the learning. It simply
43:02
does not. What cognitive science actually tells
43:05
us is that the best way to teach something depends
43:07
almost entirely on the material being taught,
43:10
not a fixed neurological style of the student.
43:12
That makes a lot of sense. If you are teaching
43:14
the geography of South America, everyone needs
43:16
a visual map, whether they think they are an
43:18
auditory learner or not. If you are teaching
43:20
the cadence of a Shakespearean sonnet, everyone
43:23
needs to hear it spoken out loud. You can't just
43:25
read about the rhythm. Right. Furthermore, we
43:28
know that learning is vastly improved by active
43:30
recall. forcing the brain to retrieve information
43:33
rather than passively absorbing it in your preferred
43:36
medium. The learning styles myth wastes an immense
43:40
amount of educational resources trying to categorize
43:42
students into fixed boxes that biologically do
43:45
not exist. That is going to fundamentally shock
43:46
a lot of listeners. Yeah. Okay. Our final myth
43:50
to bust, myth number four, the fMRI. glowing
43:53
dot hype. Ah, the glowing dots. This addresses
43:57
those incredibly flashy news headlines we see
43:59
seemingly every week. Scientists find the exact
44:02
brain region for love, or researchers locate
44:05
the Biological Center for Conservative Political
44:06
Thought. And the article is always accompanied
44:09
by a picture of a gray brain with one brightly
44:11
glowing orange dot in the middle. We most actively
44:14
caution everyone to view those specific headlines
44:16
with an extreme degree of skepticism. Why? Isn't
44:18
the machine accurate? Functional MRI, or fMRI,
44:22
is an incredibly valuable, sophisticated tool
44:24
for neuroscience, but its findings are constantly
44:27
oversimplified and exaggerated by the popular
44:30
press. To understand why, it is vital to understand
44:34
what an fMRI machine is actually measuring. What
44:37
is it measuring? It does not directly measure
44:39
neural activity. It cannot see neurons firing
44:42
action potentials. If it isn't seeing the electricity,
44:45
what is the machine actually detecting? It measures
44:48
blood oxygenation. It operates on a principle
44:50
called the BOLD -D signal blood oxygen level
44:53
dependent. How does that relate to thinking?
44:55
The theory is sound. When a specific brain region
44:59
is working incredibly hard, those sodium potassium
45:01
pumps are burning energy, so that region needs
45:03
more oxygen. The circulatory system responds
45:06
by sending more oxygenated blood to that specific
45:08
area. Okay, I followed. The massive magnets in
45:11
the fMRI can detect the difference in magnetic
45:13
resonance between oxygen -rich and oxygen -poor
45:17
blood. So the glowing orange dot is just showing
45:19
where the blood is flowing. Exactly. But blood
45:22
flow is a delayed indirect proxy for thought.
45:25
It operates on a massive delay of several seconds.
45:28
Neural activity. Those electrical dominoes happens
45:32
in milliseconds. That's way behind. Therefore,
45:34
fMRI has very poor temporal resolution. It cannot
45:38
capture the split -second, incredibly complex
45:41
dialogue happening across the entire brain. It
45:43
just captures a blurry, delayed snapshot of blood
45:46
pooling. And this brings us to one of the most
45:49
hilarious yet profoundly important. cautionary
45:52
tales in the history of modern neuroscience.
45:55
The dead salmon study is absolute legend in the
45:58
field and a masterclass in scientific skepticism.
46:00
What exactly did they do? A group of researchers
46:02
wanted to demonstrate to their colleagues the
46:04
massive statistical pitfalls inherent in analyzing
46:07
fMRI data. An fMRI scan generates an enormous,
46:10
almost unmanageable amount of data. It mathematically
46:13
splits the 3D space of the brain into tens of
46:15
thousands of tiny three -dimensional pixels called
46:18
voxels. So you have tens of thousands of data
46:20
points for every single scan. Precisely. And
46:22
when you run a standard statistical analysis
46:25
looking for fluctuations across tens of thousands
46:28
of voxels just by pure mathematical chance, random
46:31
background noise will occasionally clump together
46:33
and look like a genuine signal. If you look at
46:36
enough static, you'll see a shape. Exactly. If
46:39
you don't aggressively correct your math for
46:41
multiple comparisons, you will find false positives
46:43
everywhere. So to prove this point, what did
46:46
this research team do? They went to a local fish
46:49
market and purchased a mature Atlantic salmon,
46:51
a deceased salmon. They placed the dead salmon
46:55
into the fMRI scanner. They then ran a standard
46:57
experimental protocol. They showed the dead salmon
47:00
a series of photographs depicting human individuals
47:02
engaged in various social situations, and they
47:05
explicitly asked the salmon to determine what
47:08
emotion the humans in the photos were experiencing.
47:11
I know this is serious science, but the mental
47:13
image of a dead fish being asked to evaluate
47:15
human emotions inside a multi -million number
47:18
medical scanner is incredible. And this was a
47:20
real peer -reviewed paper. It was a very real,
47:23
highly influential paper. Because when they ran
47:26
the standard, uncorrected statistical analysis
47:29
that many researchers around the world were routinely
47:31
using at the time, the scan showed an apparently
47:34
significant cluster of brain activity in the
47:37
dead salmon's brain cavity precisely while it
47:40
was viewing. photographs the machine found a
47:42
glowing orange dot of empathy in a dead fish
47:45
because of random uncorrected statistical noise
47:48
it brilliantly and undeniably highlighted the
47:52
vital need for rigorous statistical correction
47:54
in a way that a dry mathematical methods paper
47:57
never could have achieved so fmri isn't useless
48:00
it's just easily misinterpreted exactly The point
48:03
of the salmon is not that all fMRI is useless,
48:06
properly corrected fMRI research is eliminating.
48:09
But when you see a headline claiming scientists
48:11
found the exact isolated region for love, remember
48:14
the salmon. Highly complex human behaviors like
48:17
love, political affiliation, or moral decision
48:19
-making are almost never localized to one single
48:22
glowing dot. They are the emergent result of
48:24
massively distributed processing across those
48:26
100 trillion synapses. Which brings us to the
48:30
final and frankly the most philosophically staggering
48:32
section of our deep dive today. We call it the
48:36
ultimate mystery. We have mapped the physical
48:39
anatomy of the firm jelly. We traced the electrical
48:42
dominoes and the chemical fairies. We busted
48:44
the myths of left brains and learning styles.
48:47
We can explain a staggering, triumphant amount
48:50
about how the brain mechanically processes information.
48:54
The scientific progress over the last century
48:57
is genuinely breathtaking. We can literally trace
49:00
a visual signal step by step. Follow the way
49:02
through. Yes. We can track photons hitting the
49:05
retina, watch the electrical conversion, follow
49:07
the signal down the optic nerve, to the thalamus,
49:10
to the back of the occipital lobe. We can observe
49:12
processing stages that extract geometric edges,
49:15
then detect motion, then integrate color, and
49:17
finally recognize a specific human face. That's
49:20
incredible. We can implant electrodes and watch
49:23
memories physically consolidate during REM sleep.
49:25
We can even hook a person up to a scanner and...
49:29
by observing motor cortex preparation, accurately
49:31
predict that they're going to push a button seconds
49:34
before they consciously report making the decision
49:36
to push it. But despite all of that incredible
49:38
granular mechanical progress, we inevitably hit
49:42
a massive glaring wall. And this wall is what
49:46
the philosopher David Chalmers famously framed
49:48
as the easy problems versus the hard problem
49:52
of consciousness. And we must clarify immediately
49:54
that when Chalmers calls them the easy problems,
49:58
He is being quite ironic because they aren't
50:00
easy at all. The easy problems are actually enormously
50:03
difficult. Nobel Prize winning questions about
50:06
biological mechanics. They are questions about
50:09
how perception works, how the brain sustains
50:11
attention, how memory is molecularly stored,
50:14
and how motor behavior is generated. But they're
50:17
categorized as easy because they are fundamentally
50:19
just matters of function, right? We know what
50:21
the biological puzzle pieces look like. We just
50:23
have to dedicate enough decades and supercomputers
50:25
to figuring out exactly how they snap together.
50:28
Exactly. They're ultimately complex engineering
50:30
problems. Given enough time, technology, and
50:32
funding, we're making steady observable progress
50:35
on them we are solving the mechanics but the
50:37
hard problem is in a completely different almost
50:40
unapproachable category what is the hard problem
50:43
the hard problem asks a question that neuroscience
50:46
cannot currently answer why is any of this highly
50:49
complex biological function accompanied by a
50:51
subjective internal experience why does it feel
50:54
like anything at all If we go back to the visual
50:57
processing example, I can entirely understand
51:00
the mechanical chain reaction of a 700 nanometer
51:03
wavelength of light hitting my eye, generating
51:05
an electrical domino wave, sending glutamate
51:08
fairies across a synapse, and lighting up a region
51:10
in my occipital cortex. You understand the mechanics
51:13
of the data transfer. Right. But why does that
51:16
cold, dark, silent data processing suddenly and
51:19
magically result in the vibrant, undeniable,
51:22
subjective, internal feeling of seeing the color
51:25
red? Why is there something that it is like to
51:28
be you? Why doesn't all this brilliant biological
51:30
processing just happen entirely in the dark without
51:33
awareness, exactly like a silicon computer processing
51:36
a complex line of code? Like a camera. A digital
51:39
camera can detect red light perfectly, measure
51:42
it and categorize it. But we do not believe the
51:44
digital camera experiences the redness. It has
51:47
no inner life. So how does the wet, firm jelly
51:50
of the brain generate an inner life? So what
51:52
are the theories? How do scientists and philosophers
51:55
even begin to approach a solution to the hard
51:58
problem? It is the frontier of human thought,
52:01
and there are several competing theories, but
52:04
it is vital to note that there is absolutely
52:06
zero scientific consensus. There are two major
52:10
frameworks that often lead the discussion. That's
52:12
the first one. The first is called global workspace
52:14
theory. This framework proposes that consciousness
52:18
acts essentially like a theatrical spotlight
52:20
on a dark stage. It theorizes that consciousness
52:23
arises when a specific piece of information is
52:26
broadcast widely across the brain's networks,
52:28
becoming simultaneously available to many different
52:31
cognitive systems at once. Your memory system,
52:33
your verbal reporting system, your motor action
52:35
system. So when everyone gets the memo, you feel
52:38
it. Basically. When the information enters that
52:40
global workspace, you become subjectively conscious
52:43
of it. Okay, so in that view, consciousness is
52:46
just a state of widespread internal broadcasting
52:48
of data. What is the other major approach? The
52:51
other major framework is called integrated information
52:54
theory, or IIT. This is a much more abstract,
52:57
mathematically driven approach. How does it work?
53:00
It attempts to literally quantify consciousness.
53:03
It proposes that consciousness is a fundamental
53:06
property of complex systems, and it arises based
53:10
on how densely integrated a system's information
53:12
is. The more integrated the network, the more
53:15
the parts mutually influence each other in a
53:18
way that cannot be reduced to individual components,
53:20
the higher its mathematical score of consciousness,
53:23
a value they denote with the Greek letter phi.
53:26
But neither of these theories has definitively
53:29
won out or solved the mystery. Not even close.
53:32
Both theories have highly passionate advocates
53:34
and incredibly fierce, serious critics. What
53:38
do the critics say? Global workspace theory might
53:40
elegantly explain the mechanism of how we access
53:42
information, but critics forcefully argue it
53:45
still doesn't explain the subjective feeling
53:47
of the information. It just explains the broadcasting.
53:50
And the math one. Integrated information theory
53:52
provides elegant, complex math. But its philosophical
53:56
implications, which suggest that even highly
53:58
integrated inanimate objects might possess a
54:01
tiny fraction of consciousness, are highly controversial.
54:04
That is wild. The deeply humbling truth is that
54:07
we do not even have an agreed upon scientific
54:10
framework for what a final satisfying answer
54:13
to the hard problem would even look like. It
54:15
remains a profound, beautiful mystery. So as
54:18
we finally wrap up this expansive deep dive,
54:21
let's take a breath and look at the grand summary
54:23
of what is happening inside your head at this
54:25
exact second. It's quite a lot. billion microscopic
54:30
workers. They are frantically communicating across
54:34
a dizzying network of roughly 100 trillion chemical
54:37
and electrical synapses. This vast, dense, metabolically
54:41
expensive network is meticulously organized into
54:44
specialized but heavily interconnected regions
54:46
from the deep survival focused brainstem all
54:49
the way up to the crumpled pizza size wrapper
54:52
of the cortex. And this firm jelly is continuously
54:55
physically reshaping its own structural connections
54:57
every single day. time you learn a fact, practice
55:00
a chord, or experience something new. And from
55:03
this incredibly complex, wet biological machinery,
55:06
we somehow derive absolutely everything that
55:09
matters to human existence. We get the vivid
55:12
colors of vision, the complexity of spoken language,
55:15
and the capacity for multi -decade planning.
55:18
We get the profound, subjective, gut -wrenching
55:21
experiences of love, joy, and grief. And perhaps
55:25
most astoundingly of all, from this three pound
55:28
mass of matter arises a mind that possesses the
55:31
astonishing capacity to turn inward and investigate
55:34
itself. Which is truly poetic when you think
55:36
about it. But that core mystery, the heart problem,
55:39
remains untouched. Why does it feel like anything
55:41
from the inside? I want to leave you, our listener,
55:44
with the final provocative thought to mull over
55:46
today as you go back to your coffee, your commute,
55:49
or your laundry. It builds right on the edge
55:51
of what we just discussed. It's a great question.
55:53
If consciousness is fundamentally a mystery of
55:55
why highly complex function produces the subjective
55:58
experience, well, could we ever truly know if
56:01
a non -biological system eventually achieves
56:04
it? Imagine we build a highly complex, deeply
56:07
integrated artificial intelligence system, a
56:09
machine architecture that perfectly mathematically
56:12
mimics the exact signaling and the 100 trillion
56:15
connections of our own brain. A perfect silicon
56:17
replica. Exactly. If it processes information
56:20
with the exact same integrated complexity, does
56:23
that silicon machine suddenly start feeling something
56:25
from the inside too? Or is the subjective magic
56:28
of feeling uniquely and exclusively tied to the
56:31
biology of our living jelly? It is a question
56:34
that pushes the boundaries of neuroscience right
56:36
into the looming future of artificial intelligence.
56:39
Thank you so much for joining us on this deep
56:42
dive. Keep wondering about that incredible complex
56:44
universe sitting quietly right inside your own
56:46
head.