Chapter 1: Behavioral Neuroscience Scope & Outlook

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Welcome back to the Deep Dive.

Today we're doing something a little different.

We are going straight to the source code of, well, human existence.

Yeah, we're stripping it all back.

We're stripping away the philosophy, the poetry, all the sentiment, and we are just looking at the machinery.

The machinery, quite literally.

We are tackling the foundational chapter, chapter one of behavioral neuroscience, the eighth edition by Breedlove and Watson.

And look, I know what you're thinking.

Textbook just sounds dry.

It implies dusty rote memorization, yeah.

But this material, it is anything but.

This is, I mean, this is basically the user manual for your own brain.

It really is.

It's the final frontier of biology.

Yeah.

And what I love about this text in particular is how it frames the whole field right from the jump.

It doesn't start with a diagram of a cell or something.

It starts with a movie.

It starts with a movie.

Spielberg's film, AI Artificial Intelligence.

Which, if you haven't seen it, is this haunting, almost futuristic fairy tale.

And it's such a crucial starting point.

So you've got this robot, a mecha named David.

He's designed to look like a 10 -year -old boy.

He has skin, he blinks, he smiles.

The kicker is that David is unique.

He's the first robot ever programmed to love.

He's built to imprint on a human mother, Monica, and just show her absolute undying devotion.

And the movie just lays this classic sci -fi question on you.

If David cries, if he begs for his mother, if he hugs her and says, I love you, does he actually feel it?

Or is it just a really, really complex script executing a line of code?

Exactly.

It's the Turing test for the heart.

Can a machine have a soul?

But here is where the authors of our source material, they just pull a fast one.

They flip it, they flip the whole script.

Completely.

They say, forget about David for a second.

Let's look at Monica.

Look at the human mother.

Is she a machine?

Is she a machine.

That is just such a heavy question to drop on page two of a science textbook.

It is, but if you follow the logic, it makes sense.

I mean, Monica isn't made of silicon and copper wire, right?

She's made of carbon and water and soul.

But she's still composed of trillions of individual cells.

And inside her skull, she has this lump of tissue that operates on electrical signals and chemical exchanges.

So when she feels love for David or guilt or grief,

those emotions, they feel like magic to her.

They feel spiritual.

But the text is arguing that at a fundamental level, those are just biological programs running on a biological computer.

Exactly.

Her neural programs, programs shaped by millions of years of evolution, are guiding her behavior just as rigidly as David's code guides his.

So if we accept that premise that Monica is just a carbon -based version of David, it's fundamentally unnerving.

It is.

Because if she's a machine, where's the ghost in the machine?

If every tear she sheds is just a specific cascade of neurotransmitters and saline kicked off by the hypothalamus, does that grief mean less?

And that's the tension.

The authors are forcing us to confront this idea that biological doesn't mean magical.

It just means it's incredibly complex.

And that is the mission statement of this whole field of behavioral neuroscience.

We're trying to pop the hood on the Monica machine and see how the gears turn.

And when we say gears, we're not talking about a few cogs here and there.

The specs of this machine are?

Well, they're hard to even wrap your head around.

Oh, absolutely.

We have to look at figure 1 .1 in the text, the brain by numbers.

The scale is astronomical.

I mean, if you're listening to this, I really want you to try and visualize these numbers, because they're just staggering.

OK, let's start with the neurons, the basic nerve cells.

You have about 86 billion neurons in your brain.

86 billion with a B.

Isn't that more than the stars in our galaxy?

This is the same ballpark.

Yeah, it's a galactic number.

But neurons, they aren't islands.

They talk to each other.

They connect at these little gaps called synapses.

And because each neuron can connect to thousands of others, you end up with, well, trillions of connection points.

Trillions of synapses.

Think of it like a forest.

You have 86 billion trees, the neurons.

But the real magic, the information, isn't in the trees themselves.

It's in the trillions of points where the leads and branches touch.

It's the handshake, not the hand.

And then you have the wiring, the axons.

These are the slender extensions that send the signals out from the neuron.

The text has a visualization here that just, it blew my mind.

It's my favorite stat in the whole chapter.

It's incredible.

If you took all the axons from one single human brain.

Just one person.

Just one person, and you laid them end to end,

those axons would wrap around the circumference of the Earth four times.

Four times.

All of that packed inside your skull right now?

It just shows you the sheer density of the network.

And the processing power.

They estimate the brain performs something like 10 to the 16th calculations per second.

I don't even know what that number looks like.

What is that, 10 quadrillion?

It's a supercomputer.

But here's the difference.

A supercomputer that could do that kind of math would need a massive cooling tower and probably its own power plant, your brain.

It runs on about 25 watts of power.

Like a dim light bulb.

Exactly.

It's a squishy, wet supercomputer running on the energy of a refrigerator light bulb.

And yet,

it produced the Sistine Chapel, the theory of general relativity, and the ability to even understand what we're talking about right now.

So this field behavioral neuroscience used to be called biological psychology, right?

Yeah, biopsych for sure.

But the name changed because the field just, it exploded.

It's not just a branch of psychology anymore.

The text calls it an umbrella and figure 1 .2 shows this visually.

It's more like a hub.

You can't do this work in a silo.

To understand the brain, you need psychology, sure.

But you also need biology, physiology, you need engineering, neurology, psychiatry, and now more and more, you need computer science.

So we have this massive, complex biological machine.

How do scientists actually study it?

I mean, you can't just open it up and watch the thoughts run around.

Well, you can't see thoughts, no.

But you can see the machinery in action.

The text outlines five viewpoints of behavioral neuroscience.

You could think of these as like five different camera angles on the same scene.

Okay.

If you want to understand a car, right?

You can look at the engine, that's the mechanism.

You can look at the blueprint, that's description.

You can look at the old Model T it evolved from.

That's evolution.

Right.

You can watch it being built on an assembly line, that's development.

Or you can learn how to fix it when it breaks down and that's application.

Let's walk through these because this is really how scientists organize their thinking.

So viewpoint number one is describing behavior.

This seems really simple to say what the animal is doing, but it's the foundation of everything.

You have to be incredibly precise.

And there are two ways to describe behavior, structurally and functionally.

Okay, break that down for us.

A structural description is all about the mechanics.

If I'm describing your arm moving, I'm talking about which specific muscles are contracting, the angle of your elbow, the velocity of your wrist.

It's purely physical.

And functional.

Functional asks, what's the goal?

What's the purpose?

Is that limb moving so you can walk?

Is it moving to run?

Or, and this is a surprisingly modern example from the text.

I saw this in the notes.

Is it moving to text or sext?

I have to say, I appreciate that the textbook authors acknowledge sexting as a distinct behavioral function.

It feels very current.

It's a totally valid functional distinction.

The structural description, your thumb tapping on a glass screen, that might be identical whether you're emailing your boss or you're sexting a partner.

But the goal is very different.

Very different.

The functional description and crucially, the brain regions involved in the motivation and the reward,

they would be very, very different.

Okay, so description gives us the what?

Viewpoint number two takes us backward in time, the evolution of behavior.

This is all based on Darwin, of course.

And it looks at two main things, continuity and difference.

Continuity implies things we share with other animals because of a common ancestor.

Right.

Because we all evolved from common ancestors, we share certain, let's call it hardware.

For example, the electrical signal in a neuron, the action potential,

is basically the same in a jellyfish as it is in a human.

Wait, so a jellyfish thinks like us.

Not thinks like us, no, but the method of sending the message is the same.

Nature found a solution that worked.

Use electricity to send a message.

And it just stuck with it for millions of years.

The old, if it ain't broke, don't fix it approach.

That's exactly it.

And that's why we can study a giant squid or a sea slug to learn fundamental things about how human nerves work.

There's a famous maxim in biology.

What is true of E.

coli is true of the elephant.

Meaning the basic cellular mechanics are conserved across species.

For the most part.

But, and this is important, we also have the second part, difference.

Species specific adaptations.

We don't use echolocation like a bat.

We don't have the incredible sense of smell of a dog.

Right.

So while that E.

coli to elephant idea is useful, the text warns us not to take it too literally.

We're not just big bacteria.

Okay, viewpoint number three, development.

Or the fancy word for it is ontogeny.

Ontogeny is just a scientific term for the whole process of growing up and growing old.

From conception to death.

Why does watching something grow help us understand how it works?

Because when you watch the machine being built, piece by piece,

you can see when different systems come online.

For example, the text mentions that learning ability in monkeys increases dramatically over the first few years of their life.

Okay.

That tells us that the complex brain circuits you need for, say,

difficult tasks, they take a long time to wire up and mature.

They're not ready at birth.

And they highlight a really fascinating distinction in rodents here too.

Oh yeah, this is so cool.

Young rodents, they can learn things really, really fast, but they forget them just as quickly.

Older rodents, they remember things for much longer.

So the babies are sponges, but they're leaky sponges.

Kind of, yeah.

But what that suggests to a scientist is that the mechanism for learning something new and the mechanism for storing it in long -term memory might actually be two different physiological processes that develop at different speeds.

By watching them develop, we can tease those two things apart.

That makes sense.

Okay, viewpoint four, biological mechanisms.

This is the how.

This is treating the organism strictly as a machine.

We want the wiring diagram.

Which hormones are being released?

Which electrical circuits are firing when a certain behavior happens?

So if you're looking at reproductive behavior, you're not asking why in a poetic sense.

No, you're asking which specific test doctrine receptors are activating in the hypothalamus to trigger that behavior.

It's all about the mechanics.

And then finally, viewpoint number five, applications.

The so what question.

This is all about applying our knowledge to fix the machine when it breaks, when there are dysfunctions.

Things like Alzheimer's or stroke recovery or depression.

Exactly.

Gene therapy to improve memory, speech therapy after a brain injury.

The ultimate goal of all this basic science is to improve the human condition.

So those are the five viewpoints.

But knowing the viewpoints doesn't tell us how you actually get the data.

The text introduces three specific approaches to experimentation.

And there's this great circle diagram figure 1 .3 that shows how they all feed into each other.

Yeah, that circle diagram is basically the roadmap for every experiment you will ever read about in neuroscience.

Let's break down the three stops on that map.

Stop number one, somatic intervention.

Soma means body.

So in a somatic intervention, the scientist directly manipulates the body or the brain.

That's our independent variable.

Okay.

Then we look for a change in behavior and that becomes our dependent variable.

So we change the body and we watch the behavior.

Correct.

A simple example.

You drink a triple shot of espresso.

You are introducing a chemical caffeine into your body.

That's a somatic intervention.

And the result?

Your hands get shaky, you talk faster, maybe you feel more anxious.

That's the behavioral change that resulted from the change you made to the body.

Okay, that makes sense.

But in the lab, it's obviously a bit more precise than just coffee.

A little bit, yeah.

The text mentions electrical stimulation.

There was a famous case where a researcher stimulated a specific part of a patient's brain during surgery.

And she didn't just twitch her arm or something, she laughed.

Like a reflex, muscle spasm.

No, and that's the spooky part.

She actually reported finding whatever she was looking at to be amusing.

The electrical signal did just make her laugh.

It made her feel the emotion of humor.

They poked the humor button in the machine.

Wow, or like cutting a nerve connection and seeing what behavior stops.

That's a classic example of somatic intervention.

Okay, so that's stop one.

Stop two on the map is behavioral intervention.

This is just the flip side, right?

Exactly.

Here we manipulate the experience or the environment, that's the independent variable.

And we look for changes in the physical body or the brain.

That's our dependent variable.

So we mess with the environment and we check the hardware.

You got it.

For example, you put a male animal in the presence of a female.

The behavior is just seeing the female.

That's the intervention.

Right, but that simple visual input causes a cascade of changes.

His hormone levels spike.

The experience caused the body's chemistry to change.

Or showing someone a picture.

Yeah, you show a person a visual stimulus like a flashing checkerboard and you put them in an MRI scanner.

You can physically see changes in blood flow to the visual processing areas in the back of the brain.

The experience changed the organ.

And the maze training example.

This one is so important, it's a classic.

You train a rat to run a maze, you teach it something.

Afterward, you look at its brain under a microscope and you can find actual physical anatomical changes in the nerve cells in the learning and memory centers.

The experience of learning physically altered the structure of the brain.

We have to come back to that because the implications of that are just, they're huge.

But first, let's hit stop three.

Correlation.

Ah, yes.

The tricky one.

Correlation is simply measuring how a body measure varies with a behavioral measure.

Does brain size correlate with intelligence?

Do hormone levels correlate with levels of aggression?

But the text puts up a big flashing neon warning sign here.

Correlation does not prove causation.

I feel like we hear that mantra all the time.

Correlation isn't causation.

But can you explain why it's such a trap in neuroscience specifically?

Okay, the text uses the study partner analogy, which I think is brilliant.

Imagine you do a study and you find that college students who have study partners tend to get higher exam scores.

That seems logical.

Having a partner helps you learn the partner caused the high score.

Well, that's one hypothesis.

But what if people who are already really smart and highly motivated are just the types of people who are more likely to seek out a study partner in the first place?

Ah, so the intelligence caused the partnership, not the other way around.

Or what about a third factor?

Maybe students who come from wealthy families don't have to work a part -time job.

So they have more free time to find a study partner and more free time to study.

So money causes both the partnership and the high score.

So you just don't know which way the arrow of causality is pointing.

You have no idea.

Now apply that to the brain.

We have very strong correlational data showing that people with schizophrenia often have enlarged cerebral ventricles, those fluid -filled spaces in the brain.

But did the enlarged ventricles somehow cause the schizophrenia?

Or did the disease process of schizophrenia cause the brain tissue to shrink, which in turn made the ventricles look bigger?

Or maybe the medication they took for years caused the change.

Exactly.

You can't tell from a single brain scan.

Correlation just tells us, hey, look over here.

These two things are linked somehow.

It gives us a great hypothesis.

But to prove it, you usually need to go back and design a somatic or a behavioral intervention experiment.

Okay, I wanna double -click on that idea of behavioral intervention.

The idea that experience changes the brain.

This is the concept of neuroplasticity.

This is, in my opinion, the single most important takeaway for anyone listening to this.

If you learn nothing else, learn this.

Your brain is not set in stone.

It is not fixed.

How does the text officially define it?

Neuroplasticity is the ability of the nervous system to change in response to experience or the environment.

And looking at the history, this wasn't always the accepted view, was it?

Not at all.

But William James, the famous psychologist from way back in 1890, he suspected it.

He described plasticity as having a structure weak enough to yield to an influence, but strong enough not to yield all at once.

That's surprisingly poetic for a science text.

It is, isn't it?

But what's amazing is that modern science shows the brain is even more plastic than James could have ever imagined.

We're not just talking about forming habits over years.

We are talking about physical structures changing minute to minute.

Like the dendritic spines.

Yes.

These are tiny little bumps on the branches of neurons where connections happen.

And they aren't static.

They're in constant motion.

They literally change shape in seconds.

They are dancing, growing, shrinking based on what your brain is doing right now.

The text has this great meta -commentary about being in a lecture hall.

It says the whole point of teaching, or frankly a podcast like this, is to physically alter the listener's brain.

That is the biological goal, yes.

If you remember anything from this discussion tomorrow, it's because we have physically altered the protein structure and the neural connections in your brain.

I have to be honest, the idea that I'm physically rewriting someone's brain structure just by talking into this microphone,

that sounds like a superpower.

Or a curse, it feels like hyperbole.

That sounds like poetry, right, or metaphor.

But physically, molecularly, it is literally true.

You are a brain sculptor.

Every conversation you have creates a trace.

Okay, let's look at the evidence for this though, because brain sculpting sounds nice, but show me the data.

There's the famous rat study, figure 1 .4.

This is a foundational study.

So researchers took young male rats and split them into two groups.

Group A is the solitary confinement group.

They live alone in a standard cage.

No friends, no stimulation.

A sad life for a rat.

And group B.

Group B is the party group.

They live together in an enriched environment.

They get to play, sniff each other, roughhouse, interact.

And what happened to their brains?

They looked at a specific part of the amygdala, the posterior dorsal medial amygdala.

This is an area involved in processing odors, which is a huge part of rat social life.

And the isolated rats, that brain region was significantly smaller.

It actually atrophied or just didn't grow?

It was stunted.

The social experience, the simple act of playing, physically bulked up that part of the brain.

The lack of social input caused the hardware to wither.

That is wild, but that's rats.

Critics will always say, but rats aren't people.

Do we have human examples?

You do.

And this one I think is even more mind -bending because it involves nothing more than pure thought.

This is figure 1 .5, the pain expectation study.

Okay, walk us through it.

Researchers had human volunteers put their hands in water that was exactly 47 degrees Celsius.

That is hot.

I mean, not boiling, but definitely painful.

It hurts.

But here's the trick.

They split the people into two groups.

They told group one, this is gonna be moderately hot.

They told group two, this is gonna be very hot.

But the water temperature was the exact same for both groups.

Identical.

The physical sensory input from the hand to the brain was exactly the same.

But they scanned their brains while this was happening.

Ew.

The group that expected more pain showed significantly higher activation in a brain region called the anterior cingulate cortex.

And that's one of the brain's main pain processing centers.

It is.

And crucially, not only did their brain light up more, they reported feeling more pain.

So the expectation, a social psychological thing you were told, it changed the physical reality of the brain's response to the exact same stimulus.

Yes.

Your prediction of the future literally shaped your physical reality in that moment.

This connects back to that idea of levels of analysis.

You can't just look at the neurons in a vacuum.

You have to look at the social context they exist in.

Right.

This is the concept of reductionism.

Reductionism is the scientific strategy of breaking a system down into smaller and smaller parts to understand it.

But in behavioral neuroscience, you have to operate on a whole hierarchy of levels.

Fretzer 1 .6 shows it like a ladder.

Okay, let's climb the ladder from top to bottom.

At the very top, you have the social level.

That's individuals interacting, like the instructions in the pain experiment.

Then you go down a rum.

Then you have the organ level, the brain as a whole, the spinal cord, the eyes.

Then down to the neural systems level, like the visual cortex or the pain network.

Then the circuit level, local groups of neurons.

And it keeps going.

Yep.

Down to the cellular level, a single neuron.

Then the synaptic level, where two neurons connect.

And finally, the molecular level, the individual receptor proteins and neurotransmitters.

And the text makes a good point that we rarely need to go down to the atomic level.

We're not looking for quirks to explain why you feel sad.

Not usually, no.

But the key is that we're constantly moving up and down this ladder.

To understand that pain experiment, you needed the social level, what the researcher said, and the neural systems level, what the anterior cingulate cortex did.

If you only looked at one, you'd miss the whole picture.

Let's pivot to why all this matters so much.

We talked about applications earlier as one of the viewpoints.

This isn't just an academic exercise.

The toll of brain disorders is, it's staggering.

It's a global crisis, honestly.

The stats and the text are really sobering.

One in five people worldwide suffers from a neurological or psychiatric disorder.

One in five.

That means if you're in a room with four other people,

statistically, one of you is dealing with this directly.

And in Europe, they estimate that 38 % of the population suffers from a mental disorder in any given year.

38%.

And the costs.

In the US alone, it's over $400 billion a year.

And to put that in perspective, the text notes that the cost of treating dementia, just dementia, exceeds the combined costs of treating cancer and heart disease.

That is unbelievable.

It's the public health burden of our time.

As medicine helps us live longer, our brains are often the first thing to fail us.

And this is why this research is so critical.

We mentioned schizophrenia earlier.

Let's talk about that twin study in figure 1 .9.

This is a visual that really sticks with you.

You have two MRI scans side by side.

They are identical twins.

So identical DNA, they are genetic clones of each other.

Exactly.

But twin A has schizophrenia, and twin B does not.

And you can see the difference with your naked eye on the scan.

Oh, clearly.

In twin A, the cerebral ventricles, those fluid -filled spaces in the middle of the brain, they look kind of like butterfly wings.

They are visibly dramatically enlarged.

They're huge compared to the healthy twin.

So identical genes, but physically different brains.

Which brings us right back to that causality question.

Did the enlarged ventricles cause the disease, or did the disease process or the treatment change the structure of the brain?

We still don't know for sure.

But seeing that physical difference validates that this is a biological disorder, not a character flaw or a moral failing.

And this search for answers leads us to maybe the most uncomfortable part of the chapter,

the discussion of animal research.

Yeah, and the text doesn't shy away from this at all.

It states it very clearly.

To study the biological basis of behavior, we must study animals.

The stat is that something like 93 % of the mammals used in research are laboratory -reared rodents, mice and rats.

But it fully acknowledges the ethical conflict.

It mentions Peter Singer's book, Animal Liberation, and his argument is that research is only justified if it produces clear benefits.

Which sounds reasonable on the surface, but the scientific response is, how do you know what the benefits will be ahead of time?

Exactly.

How can you possibly predict which experiment is going to lead to the big breakthrough?

You can't.

You have to do the basic science first, you have to understand the mechanism before you can fix it.

And then on the other side of the debate, you have the extremism.

The text mentions the attacks in Santa Cruz back in 2008.

It was incredibly violent.

Animal rights activists were firebombing researchers' cars and homes.

There's a story of one researcher having to flee with her children through a second story window because her front porch was on fire.

It's a genuine war of values.

It is.

But the conclusion the text reaches is pretty clear.

Until we can build robots like David from the movie that can simulate the complexity of a living biological system perfectly,

animal research is really the only way we have to understand the living brain.

We can't model a new Alzheimer's drug on a microchip yet.

The complexity is just too high.

We've talked about the present, the methods, the conflicts.

Let's zoom out now and look at the history.

Because we didn't always know the brain was the boss.

In fact, for most of human history, humanity treated the brain like what?

Like garbage.

Literally garbage.

It's amazing.

Take us back to ancient Egypt, say 1300 BCE.

Okay, picture the scene.

You're an embalmer.

A pharaoh has just died.

You are preparing his body for the afterlife.

You carefully preserve the heart, the liver, the lungs, the intestines.

You put them in these beautiful sacred alabaster jars.

Because you believe he's gonna need them in the next world.

Right.

They're essential.

But then you get to the brain.

You don't even bother cutting the skull open neatly.

You take a long bronze hook.

And figure 1 .11 actually shows one of these hooks.

And you shove it up the corpse's nostril.

You smash through the delicate bone at the back of the nose.

And you just, you whisk the brain.

You scramble it into a slurry.

And you pull it out piece by piece through the nose.

And then what do you do with it?

You throw it away.

You discard it.

It was considered cranial packing peanuts.

Just useless waste material.

But why?

Why did they think so little of it?

Because they, like many ancient cultures, thought the heart was the seed of the soul, of intelligence, of emotion.

And you know, if you don't know the anatomy, that almost makes intuitive sense.

When I get scared or excited, my heart pounds in my chest.

My brain doesn't throb.

Exactly.

The heart gives you immediate physical feedback.

The brain is silent.

Even the Bible never mentions the brain.

Not one single time.

It talks about the heart, the liver, the bowels, as the seeds of emotion.

I love you with all my bowels.

I am so glad we switched to heart for our Valentine's cards.

Me too.

Then you have Aristotle.

I mean, one of the smartest people who ever lived.

But he doubled down on the heart theory.

He thought the brain was just a radiator.

A radiator.

A cooling unit.

He believed it existed to lower the temperature of the hot blood that he thought was being pumped up from the heart, the center of vitality.

He even argued that's why humans have such large brains, because we have such passionate hot blood that needs a lot of cooling.

So when did we start to get it right?

This very slow process.

A physician named Herophilus, the father of anatomy, started tracing nerves from the brain to the muscles.

But the big turning point was probably Galen, the Greco -Roman physician.

He treated gladiators.

Now that is a rich source of neurological data.

An incredible source of data.

He saw day in and day out that when a gladiator got a severe injury to the head, his behavior changed.

Or he lost consciousness.

He didn't lose consciousness when he got stabbed in the arm.

So Galen started to connect the brain with behavior.

He proposed that animal spirits flowed through the hollow tubes of the nerves.

Then we jump forward a thousand years to the Renaissance.

And to Leonardo da Vinci.

Da Vinci was just on another level.

He started by drawing the brain based on old, inaccurate theories.

But then he switched to direct observation.

Figure 1 .12 shows his drawings.

And you can see the progression.

He actually made a wax casting of an ox brain.

How on earth did he do that in the 1500s?

It's ingenious.

He took a syringe and he injected hot, melted wax into the ventricles of an ox brain.

He let it harden.

And then he carefully scraped away all the soft brain tissue.

He was left with a perfect three -dimensional cast of the internal fluid -filled structures.

He was the first person to see their true, complex shape.

And Michelangelo, there's a brain theory about him too, right?

Yeah, this is a great little Easter egg for art history fans.

You know the famous painting in the Sistine Chapel, God Creating Adam?

Of course.

God is reaching out his finger to Adam.

Okay, now look at the shape of the red cloak that's billowing around God and the angels.

Its outline corresponds almost perfectly to an anatomical cross -section of the human brain.

You're kidding me?

Nope.

Many art historians believe Michelangelo, who did dissections, was hiding a message in plain sight that the divine gift God is giving to Adam isn't just life, it's intellect.

It's the brain.

That is amazing.

Okay, then comes René Descartes in the 17th century.

We can't talk about neuroscience without talking about Descartes.

Descartes gave us dualism.

He was a deeply religious man, and he couldn't reconcile the mechanical, predictable body with the free, spiritual mind or soul.

So he basically said, there are two different things.

The body is a machine, but the soul is non -material and separate.

But they had to connect somewhere, right?

That was the problem.

He proposed they met at the pineal gland.

The pineal gland.

Why there, of all places?

Well, for one, most brain structures are doubled.

You have a left and a right hemisphere.

The pineal gland is single, and it sits right in the middle.

He thought that fit with the unified nature of consciousness.

He also mistakenly thought only humans had one.

Yeah, pretty much every vertebrate has one.

But Descartes also gave us the concept of the reflex.

You can see his drawing in Figure 1 .13.

He described how if fire touches a toe,

a signal travels up to the brain and is reflected back down to the muscle to pull the foot away.

He was wrong about the details.

He thought it was all a hydraulic fluid in the nerves.

But the basic concept of a sensory motor loop was a massive breakthrough.

Huge.

It paved the way for treating the body and behavior as a mechanism that could be studied.

Which leads us to the 19th century,

and the bumps on the head,

phrenology.

Ah, phrenology.

Figure 1 .14a shows one of those classic phrenology heads.

This was the idea that you could read a person's personality and their talents by feeling the bumps on their skull.

So if you had a big bump here, you were highly amative, which meant you were passionate.

If you had a bump there, you were combative.

It was complete and utter pseudoscience.

It was wrong and worse.

It was used to justify all sorts of horrible racist and sexist stereotypes.

But, and this is a really big but, but it accidentally popularized the idea of localization of function.

The idea that specific parts of the brain do specific things.

Exactly.

The phonologists got the locations wrong, they got the method wrong, they got the traits wrong, but Paul Broca, he got it right.

Broca's area for speech.

He had a patient who could understand language perfectly, but he couldn't speak.

The only word he could say was tan.

When the patient died, Broca did an autopsy and looked at his brain.

The damage was restricted to a very specific area on the left side of the frontal lobe.

That's proof.

That's real evidence that language production lives in that specific neighborhood of the brain.

Localization was real.

Phonology was bunk, but the underlying idea had a kernel of truth.

Moving into the 20th century, we get the search for the enneagram, the physical trace of a memory in the brain.

And we get Donald Hebb.

He is an absolute titan of the field.

In 1949, he proposed the mechanism for how neurons could learn together.

The famous phrase, neurons that fire together, wire together.

The Hebbian synapse.

He theorized that if one neuron repeatedly helps to fire another neuron, the connection, the synapse between them, physically strengthens.

That is the biological basis of learning.

That's how the machine actually programs itself based on experience.

Which brings us to the biggest mystery of all, one that we still haven't solved, consciousness.

The so -called hard problem.

The text mentions Adam Zeman's criteria that consciousness allows for planning, it's bound to brain activity, and so on.

But explaining why it feels like anything at all is the hard part.

It is.

We can map the activity.

We can put you in a standard and say, okay, when you see the color blue, this specific pattern of activity happens in your visual cortex.

But we have absolutely no way to explain why that pattern of electrical activity feels like the subjective experience of blueness to you.

Why doesn't it feel like yellowness or smell like cinnamon?

We are nowhere near solving that.

But the text does take a moment to bust a very persistent myth here, the 10 % myth.

Oh, the idea that we only use 10 % of our brain?

It is just patent nonsense.

I blame Hollywood movies for that.

Lucy, limitless.

It makes for a great movie plot, but it's terrible science.

Modern brain scans show that the entire brain is active almost all the time.

Even when you're just resting or daydreaming, the whole board is lit up.

Evolution would never maintain an organ that consumes 20 % of your body's total energy if you were only using 10 % of it.

It's just too expensive to keep around if it's not being used.

So what does the future of this field look like?

Big data, projects like the human brain project.

Trying to map the whole thing, synapse by synapse.

Essentially trying to create a complete digital simulation of the human brain.

But figure 1 .16 really puts this challenge in perspective.

To map a single human brain, we would need computing power measured in exaflops and data storage measured in zettabytes.

Those sound like made up science fiction words.

They're real units, but they represent amounts of memory and speed that are vastly beyond what we currently have.

Still, the field is growing exponentially.

The number of scientific papers published with the word neuroscience in them is just skyrocketing every year.

So to wrap all of this up, we started with David and Monica, the robot and the mother.

And I think we've learned that the distinction between them isn't as clear cut as we might like to think.

The brain controls our behavior, yes, but our behavior and our experiences constantly alter our brain.

It's a reciprocal loop.

We are biological machines, but we're biological machines that can reprogram ourselves through experience, through social interaction, through learning.

And while we are still a very, very long way from building a machine that is truly conscious, if that is even possible,

understanding the biological machine inside our own heads is, for my money, the most exciting quest in all of science.

So the next time you feel love or you feel pain or you just learn a new fact from a podcast.

Remember that you just physically changed the shape of your brain.

And maybe asking, are we just machines?

Is the wrong question.

Maybe the right question is, what kind of incredible machine is it that can feel?

That is a wrap for today's Deep Dive into chapter one of behavioral neuroscience.

Thanks for listening to this last minute lecture, Deep Dive.

Keep learning.

See you next time.