Right now, inside your head, there are 86 billion
tiny wet cells just shouting at each other in
the dark. Yeah, it's a completely chaotic electrochemical
storm. Right. And somehow that exact same biological
chaos, I mean, that foundational architecture
of the human mind is currently being used to
like drive cars and beat chess grandmasters and
write computer code. It is arguably the most
profound crossover between biology and computer
science in human history. We basically looked
inside ourselves, figured out the wiring, and
then decided to just recreate it in silicon.
Which brings us to today's custom tailored deep
dive designed specifically for you. We're working
from a really comprehensive Wikipedia article
on neural network. It's a massive topic. Oh,
massive. And our mission today is simple. We
want to cut through the heavy, intimidating jargon
of artificial intelligence and discover the fascinating
aha moments that link the swishy biology of the
human brain to the cutting edge AI shaping our
world today. Because the reality is the mechanics
behind all this aren't magic. No, not at all.
They're logical. They're traceable. And once
you understand the underlying mechanism, the
whole landscape of modern technology just makes
a lot more sense. OK, let's unpack this. Because
we hear the term neural network thrown around
constantly, right? It's the ultimate tech buzzword.
But what are we actually talking about at a foundational
level? So at its absolute most basic, a neural
network is really just a group of interconnected
units. We call them neurons. And they just send
signals to one another. That's it. That's it.
That is the foundational concept. Now, Those
units can be actual biological cells, like the
ones in your brain right now, or they can be
purely mathematical models sitting inside a computer
program. But the architecture is exactly the
same. You have units, connections, and signals.
And there's a massive paradox right at the center
of this that really stood out in the source material.
Because an individual neuron on its own is incredibly
simple. Well, very simple. It basically just
takes in a signal, reaches a threshold, and passes
a signal along. I mean, it doesn't possess intelligence.
But you put a bunch of them together in a network.
And that's the power of immersions right there.
A single neuron is just a biological switch.
But when you link millions of these switches
together, they can perform astoundingly complex
tasks. It's crazy. But to really grasp how the
artificial versions pull this off, we have to
understand the original blueprint. We have to
look at the biology first. Right, because before
neural networks lived in computers, they lived
inside us. And they still do. A biological neural
network is a very real physical structure. It's
a population of biological nerve cells, the neurons,
chemically connected to each other by structures
called synapses. And the sheer density of this
web, it's just hard to overstate. Yeah, a single
neuron in your brain might be connected to hundreds
of thousands of other neurons through these synapses.
It's a phenomenally dense, tangled web. And across
that web, they are constantly firing electrochemical
signals, which the source calls action potentials,
to their connected neighbors. Right. But a common
misconception is that this is just a blind chain
reaction, like a row of dominoes falling over.
Like one hits the next, hits the next. Exactly.
But it's far more dynamic than that. A biological
neuron generally serves one of two primary roles
when it sends a signal. It's either excitatory
or it's inhibitory. Wait, if an individual neuron
is simple, how do we actually get complex thoughts
from that? Is it like a stadium wave? A stadium
wave? Yeah, or like a massive game of telephone
where some people are shouting to keep the message
going and others are trying to quiet it down?
Actually, that analogy works perfectly with the
source material. It's exactly like that. The
excitatory role is the people shouting, right?
Yeah. Amplifying and propagating the signals.
They push the receiving neuron closer to its
firing threshold. OK, so the inhibitory role.
Those are the people trying to quiet it down.
They actively suppress the receiving neuron,
making it less likely to fire. So the receiving
neuron is just sitting there tallying up all
the shouting and all the shushing it's getting
from its thousands of neighbors. Right. And all
complex thought. All memory, all brain activity
is simply the result of this constant balancing
act between excitatory and inhibitory signals
across massive populations of cells. And it scales
up too. The source mentions you start with small
groups called neural circuits. Yes, very small
local connections. And then you link those up
into large scale brain networks. And eventually
those networks have to actually interact with
the physical world, right? They do. The signals
generated in the brain travel out through the
nervous system, eventually crossing what are
called neuromuscular junctions. Neuromuscular
junctions. Basically where the nerve meets the
muscle. The final signal tells the muscle cells
to contract, resulting in physical motion. So
for you listening right now, just think about
that. Every single time you've ever moved a muscle
or taken a breath or formed a thought, this exact
microscopic chemical communication network is
what made it happen. Millions of excitatory and
inhibitory signals tallied up just so you could
reach out and grab your phone. It's the biological
engine of everything you do. It is completely
mind -boggling. But knowing how the human brain
works brings up a huge question. How do we cross
the gap? Right, from biology to machines. Yeah,
from biological biology to mathematical models
in machines. Because honestly, I always assumed
neural networks were a 21st century Silicon Valley
invention. You know, a couple of engineers in
a garage cracking a new algorithm. That is such
a common assumption, but the historical bridge
is much longer. and honestly much older than
most people realize. We actually have to go all
the way back to the 19th century. Right, the
1800s. Yes, the 1800s. In 1873, a thinker named
Alexander Bain, and then later the psychologist
William James in 1890, they independently proposed
the foundational theory. Wow. Yeah. Long before
we had the tools to map the brain on a microscopic
level, they theorized that human thought must
emerge from the complex interactions among massive
networks of neurons. So the conceptual framework,
the whole idea that thought is a network effect
was there before we even really had light bulbs?
Precisely. And then, if you fast forward to the
1930s, a movement called connectionism started
taking hold. Researchers began actively trying
to use rudimentary artificial networks to model
biological ones. But there had to be a breakthrough
on how they actually learn, right? Yes. The monumental
breakthrough, the moment we figured out the mechanism
of how a network actually learns over time, came
in 1949 from psychologist named Donald Hebb.
Ah, Hebbian learning. I saw this in the notes.
Break down the actual mechanism for us. How does
a web of cells learn a new trick? So Hebbian
learning operates on a surprisingly intuitive
idea. It's often summarized as Cells that fire
together, wire together. Fire together, wire
together, catchy. Right. Hebb realized that neural
networks learn over time by physically strengthening
a synapse, the connection point, every time a
signal successfully travels along it. So it's
like walking through a dense field of tall grass.
The first time you walk through, it's really
difficult. There's a lot of resistance. But if
you walk that exact same path every single day,
you eventually trample the grass down. You pack
the dirt, it becomes a clear trail, and the next
time you need to cross, that path offers the
path of least resistance. What's fascinating
here is how accurately that physical metaphor
translates to the early psychological theories,
which then directly pave the way for hardware.
Because if you understand the biological rules
of learning, you can try to build a machine that
plays by those same rules. Exactly. And around
the same time, we were getting proof of how complex
biological processing could get. In 1956, Sveijetingen
discovered the function of second -order retinal
cells. Okay, second -order retinal cells. What
does that mean for the network? Basically, he
proved that biological networks process information
in distinct layers. The eye doesn't just send
raw light straight to the brain. There are intermediate
layers of cells that process and refine the image
before passing it along. Layered processing.
Okay, keep a pin in that because that becomes
huge for the math later. So the biologists are
mapping this out. When does the computer science
actually kick in? That brings us to the first
machine. In 1943, a neurophysiologist named Warren
McCulloch and a logician named Walter Pitts teamed
up to invent the perceptron. Well, the first
mathematical model of an artificial neural network.
They didn't build a physical machine yet, right?
No, just a mathematical proof showing how a simplified
artificial neuron could act as a logic gate.
OK. But then in 1957, Frank Rosenblatt took that
math and actually built a physical perceptron
in hardware. He actually wired up a physical
learning machine in the 50s. He did? using motors
and photocells. It was the first true translation
from biology to artificial hardware. That is
an incredible bridge. We go from 19th century
philosophy to biological observation to mathematical
proofs to a physical machine in 1957. But obviously
those early physical machines evolved, right?
We moved entirely into software. We did. Today,
artificial neural networks are almost universally
implemented in software to approximate what we
call nonlinear functions. Here's where it gets
really interesting, because I want to visualize
this modern architecture. If the brain is a tangled
web of cells, the artificial version sounds much
more structured, almost like a strict corporate
hierarchy. That is a very helpful way to frame
it. Let's build that out. In an artificial network,
the mathematical neurons are arranged into very
specific sequential layers. Right. So at the
very front, you have the input layer. These are
the frontline workers. They just take in the
raw data, like the pixels of a photograph, and
pass it up the chain. Exactly. Then that data
moves into the hidden layers. This is your middle
management. The hidden layers. Right. And finally,
at the very end of the line, you have the output
layer. The executives who take all the process
reports from middle management and make the final
decision. Like, this is a picture of a cat. OK.
So information flows sequentially. Input, hidden,
Output. But let's look at the actual math happening
inside one of those hidden layer neurons. It's
not a chemical pulse anymore. It's just numbers.
Yes. The signal input to each neuron is what
we call a linear combination of the outputs from
the previous layer. Okay, stop right there. A
linear combination. That sounds incredibly dense.
It sounds worse than it is. Basically every connection
between two artificial neurons has a specific
weight assigned to it. Just a number acting as
a multiplier. The receiving neuron takes every
incoming number, multiplies it by its specific
connection weight, and adds them all together.
Okay, so it's mathematically tallying the votes,
just like the excitatory and inhibitory signals
in biology. Exactly. And once it has that total
sum, it calculates the final output via an activation
function. It's just a rule book that says, based
on this sum, here's the exact number I'm passing
to the next layer. OK, so we have layers of neurons
multiplying inputs by weights, summing them up,
and passing them through activation functions.
But how does this corporate hierarchy actually
learn anything? The source uses terms like empirical
risk minimization and backpropagation to modify
those connection weights during training. That
sounds... I mean, can we just think of this as
high -tech trial and error? High -tech trial
and error is exactly what it is. If we connect
this to the bigger picture, adjusting these weights
to fit a pre -existing data set is exactly how
the network learns. So if it guesses wrong...
It figures out how wrong it was and sends a signal
backwards through the network. Back propagation.
Right. Adjusting every single weight slightly
so it makes a better guess next time. It repeats
this millions of times until the empirical risk,
the error. is minimized. And that's what training
in AI is. That's the entirety of it. And by the
way, officially, a deep neural network is just
a network with more than three layers. So an
input layer, an output layer, at least two hidden
layers. That's what deep learning means. Just
more than two hidden layers. That's it. Wow.
OK, so now that we understand both the biological
and the artificial, there's a glaring point of
divergence that we have to talk about. Because
if artificial neural networks were originally
designed to mimic our biology, Why does the source
say they are increasingly different from their
biological counterparts today? It comes down
to efficiency. While biology was the initial
inspiration, machine learning applications required
different approaches. The goal shifted from perfectly
modeling a human brain to just solving specific
artificial intelligence problems. Like, biological
brains are amazing, but they have constraints,
right? Exactly. Human brains run on very little
power and are limited by chemical diffusion.
Computers don't have those limits. So engineers
stop trying to build a perfectly accurate biological
replica and just optimize the math for data processing.
And it's a good thing they did because that shift
is what made this technology so incredibly useful
in our daily lives. The real -world applications
mentioned in the source are everywhere. Oh absolutely
everywhere. Artificial neural networks are the
backbone of modern predictive modeling. They
power adaptive control systems, like in manufacturing
robots or autonomous vehicles. And pattern recognition,
right? Like facial recognition to unlock your
phone, or handwriting recognition. Yes. There
are also the engines behind general game playing
AIs that beat human champions. And of course,
generative AI. Right. The big one right now.
So what does this all mean for you, the listener?
Well, it means that when you use a generative
AI to write an email, or the facial recognition
to unlock your phone, you are interacting with
the direct descendant of an 1873 theory about
human thought, filtered through complex math
layers. It's an incredible evolution. It really
is. So just to recap our journey today, we started
with the biological synapses and excitatory signals
happening in our heads right now. Then we crossed
the historical bridge, looking at Hebbian learning
and the perceptron. And finally, we dove into
the hidden layers of modern software -based deep
neural networks. And this raises an important
question, I think. Yeah. I want to leave you
with a final provocative thought to mull over.
The source material notes that artificial neural
networks started by copying biology, but eventually
diverged to become better at machine learning.
If artificial intelligence continues to evolve
and tackle even more complex human -like tasks
in the future, will we need to start making these
mathematical networks look more like biological
brains again? Right, with all that messy chemical
complexity. Exactly. Or will they just evolve
into a completely alien structure that we can
no longer even comprehend? It's a fascinating
thing to consider as the technology advances.
It really is. Well, thank you so much for sharing
your sources and coming along on this deep dive
with us. Keep learning and stay curious.