Neural networks: what they are, how they work, and why the name is misleading
Neural networks aren't actually like brains. Here's what they really are, how they actually work, and why understanding the difference matters.
Your Grandfather's Telephone Switchboard
Picture this: it's 1950, and your grandfather works at the telephone company. His job? Operating a massive switchboard. Hundreds of cables, thousands of little sockets, and his hands moving lightning fast to connect one call to another.
How the switchboard worked
Someone calls from the bakery to the flower shop. Your grandfather plugs a cable from socket A47 into socket B23. Call connected. Someone else calls from the library to the post office. Another cable, another connection. All day long, he's making connections, routing signals, helping information flow from one place to another.
Now imagine that switchboard could learn. Imagine that after thousands of calls, it started to notice patterns. Most calls from the bakery in the morning go to restaurants. Most calls from schools in the afternoon go to parents. Most calls from the hospital are urgent and should go through immediately.
If that switchboard could adjust itself based on these patterns, routing calls more efficiently without your grandfather having to think about each one, you'd have something very close to a neural network.
That's what a neural network really is. Not a brain. Not intelligence. Just a very sophisticated switchboard that learns which connections work best through practice.
The name "neural network" makes it sound like we're building electronic brains. We're not. We're building smart switchboards that get better at routing information through experience. Let me show you exactly how this works, using examples anyone can understand.
The Great Name Confusion (Why "Neural" is Misleading)
When scientists first built these systems in the 1950s, they looked at how the human brain works and thought, "Hey, we could build something inspired by that." They borrowed some ideas and a fancy name.
Bad move.
Why the name is misleading
Calling these systems "neural networks" is like calling an airplane a "mechanical bird." Yes, both fly. Yes, both were inspired by observing nature. But an airplane doesn't flap its wings, doesn't have feathers, doesn't need to eat worms, and doesn't migrate south for the winter.
Your real brain
- → About 86 billion neurons
- → Each one is an incredibly complex biological cell
- → Thousands of connections per neuron
- → Runs on chemistry and neurotransmitters
- → Capable of growing and changing throughout your life
An artificial "neuron"
It's a simple mathematical operation. Multiplication and addition. That's it. About as similar to a brain cell as a light bulb is to the sun. Both produce light, but one is a massive ball of nuclear fusion and the other runs on electricity from your wall socket.
So whenever you hear "neural network," just think "pattern-learning switchboard." It's less sexy, but far more accurate. And understanding what these systems really are helps you understand what they can and cannot do.
The learning switchboard
The switchboard is learning your town's calling patterns. After watching thousands of calls, it notices the bakery calls restaurants every morning around 6 AM. Tomorrow, when that 6 AM call comes in, the switchboard is ready. It has already prepared the best connection, learned from experience, adapted to the pattern. That's what these systems do. They find patterns in examples and use those patterns to make future connections faster and better.
The Building Block: One Simple Connection Point
Let's start with the smallest piece of our switchboard: one single connection point. In the fancy terminology, it's called an "artificial neuron." But really, it's just a spot where several wires come together and one wire goes out.
Imagine you're making a decision about whether to take an umbrella when you leave the house. You look at several signals:
The signals you consider
- Is the sky dark and cloudy? (Signal 1)
- Did the weather forecast say rain? (Signal 2)
- Is it the rainy season? (Signal 3)
- Are you carrying other things? (Signal 4)
But not all these signals are equally important. The weather forecast is probably more reliable than just looking at the sky. The rainy season matters more in some places than others. You might weight them mentally:
Weighting the importance
- ● Dark clouds: medium importance
- ● Weather forecast: very important
- ● Rainy season: somewhat important
- ● Carrying things: less important
Your brain does this calculation in a split second. It weighs all the signals based on importance, adds them up, and decides: umbrella or no umbrella.
What an artificial neuron does
An artificial neuron does exactly the same thing, but with numbers. Each incoming signal gets multiplied by its importance (its weight). All those weighted signals get added together. If the total is high enough, the output is "yes, activate." If it's too low, the output is "no, stay quiet."
That's one neuron. Multiply a few numbers. Add them up. Check if the total crosses a threshold. Output yes or no. No mystery. No intelligence. Just arithmetic.
Think of it like a bouncer at a club checking your age, your dress code, and whether you're on the guest list. Each factor has a different weight. Guest list? You're probably getting in. Proper shoes? Important, but not a dealbreaker. The bouncer adds up all the factors and makes one decision: in or out. That's an artificial neuron. A very simple decision-making point that considers multiple inputs with different importance levels.
Connecting the Dots: Building the Switchboard
One connection point isn't very useful. Your grandfather's switchboard had thousands. That's where the power comes from.
Imagine you're trying to recognize your friend Maria in a crowded train station. Your brain doesn't make this decision with one neuron. It uses thousands of decision points, each looking at different details:
- First level decisions: Is that a person? Is it a woman? Approximately the right height?
- Second level decisions: Does she have dark hair? Is she wearing glasses? Is that her usual coat?
- Third level decisions: Does her face match Maria's features? That walking style looks familiar. That's definitely her bag.
- Final decision: All the pieces fit together. That's Maria. Wave and call her name.
An artificial neural network works the same way. It's organized in layers, like floors in a building. Information flows from the ground floor (input layer) through several middle floors (hidden layers) to the top floor (output layer).
Let's say you're trying to teach a computer to recognize pictures of cats. Here's what happens:
-
1
Input layer (ground floor):
Receives the raw picture. Every tiny dot (pixel) in the image goes into one neuron. A small photo might have 10,000 pixels, so you need 10,000 neurons just to receive it. Each neuron holds one tiny piece of information: "My pixel is dark" or "My pixel is light."
-
2
First hidden layer (second floor):
Looks for simple patterns. Some neurons get excited when they see horizontal lines. Others notice vertical lines. Some spot curves or corners. These neurons don't know they're looking at cats yet. They just know: "I found a curved line here" or "I spotted an edge there."
-
3
Second hidden layer (third floor):
Combines those simple patterns into more complex shapes. "Hey, these curves and edges together look like a pointy ear." "These patterns arranged this way look like whiskers." "This is definitely an eye shape." Still not recognizing cats, just identifying cat parts.
-
4
Third hidden layer (fourth floor):
Assembles the parts into full features. "These whiskers, this nose shape, these pointy ears… I've seen this combination before. This is starting to look like a cat face." Now we're getting somewhere.
-
5
Output layer (top floor):
Makes the final decision. "Based on everything the other floors found, I'm 95% confident this is a cat. Could be a dog (3% chance). Definitely not a car (0.001% chance)." The neuron with the highest confidence wins, and the network announces: "Cat!"
Each layer builds on the previous one. Simple patterns become complex shapes. Complex shapes become recognizable features. Features become full objects. That's the magic trick. Not actual intelligence, just really clever layering of simple decisions.
The Learning Part (How the Switchboard Gets Smarter)
Now here's where it gets interesting. How does the network learn which connections are important?
Learning like sorting laundry
Imagine you're training your nephew to sort laundry. First time, he has no idea. He might put a red sock with white shirts. Disaster. Pink shirts everywhere. You say, "No, no, that was wrong. Red goes with darks, not whites." He adjusts his mental rules. Next time, he does a little better. Still makes mistakes, but fewer. You keep correcting him. "That's right!" "No, try again." "Perfect!" "Oops, not quite." After a hundred loads of laundry, your nephew has learned the patterns. Whites together. Darks together. Reds with darks. Delicates separate. He doesn't need you anymore. He learned through practice and correction.
Neural networks learn exactly the same way. Here's the process:
-
1
Start with random guesses
The network begins with completely random connection strengths (weights). It's like your nephew on day one. Show it a picture of a cat, and it might say "Car!" Pure nonsense. But that's okay. Everyone starts somewhere.
-
2
Show it an example
Feed the network a picture of a cat. The image flows through all the layers. Each neuron does its calculation (multiply the inputs by weights, add them up, activate or not). Eventually, the output layer makes a guess. With random weights, the guess is terrible. "Dog! No, car! Maybe… umbrella?"
-
3
Tell it the right answer
You know the correct answer (because you labeled the training pictures yourself). "No, that was a cat, not a car." The network measures how wrong it was. Guessed car with 80% confidence when it should have said cat? That's very wrong. Calculate exactly how far off each neuron was from the right answer.
-
4
Adjust the connections (backpropagation)
Here's the clever bit. The network works backward from the output to the input, asking: "Which connections contributed to this mistake? Which weights need to change?" It's like retracing your nephew's thought process. "You put the red sock with whites because you thought color didn't matter. Let's increase the importance of color in your decision." Adjust each weight slightly in the direction that would have reduced the error.
-
5
Repeat thousands of times
Show the network another picture. It makes another guess (slightly better now). Measure the error. Adjust the weights again. Repeat with thousands or millions of pictures. Slowly, gradually, the weights shift from random garbage to useful patterns. After enough practice, the network starts getting it right. Show it a cat, it says cat. Show it a dog, it says dog. It learned.
What "learning" really means
This process is called "training" or "learning," but it's really just mathematical optimization. Adjust millions of tiny numbers (weights) based on examples until the network's predictions match reality. No understanding. No consciousness. Just pattern matching through trial and error.
The more examples you show it, the better it gets. The more varied the examples, the better it handles new situations. Show it only golden retrievers, and it will struggle with poodles. Show it cats in every color, size, and position, and it will recognize cats anywhere.
Think of it like adjusting the tension on guitar strings. Too loose, wrong sound. Too tight, also wrong. You pluck a string, listen to the note, and adjust the tension slightly. Pluck again. Still not quite right. Adjust again. After many small adjustments, each string produces the perfect note. Training a neural network is just adjusting millions of "tension knobs" (weights) until the output sounds right. Except instead of music, you're producing predictions.
Deep Learning (Why More Layers Help)
You might hear the term "deep learning" thrown around. People act like it's something magical. It's not.
"Deep" just means "lots of layers." Instead of three or four hidden layers, you might have twenty, fifty, or even a hundred layers stacked up. That's it. That's the big secret.
Why bother with so many layers? Because complex patterns need complex processing.
Imagine you're teaching someone to recognize trees. With only two layers (one hidden layer), you can teach simple rules:
Simple patterns with few layers
- ✓ Green stuff on top? Probably a tree.
- ✓ Brown vertical thing below? Definitely a tree.
But what about palm trees? Pine trees? Cherry blossoms? Trees in winter with no leaves? Trees covered in snow? Bonsai trees? Tree stumps? With only simple patterns, you'll miss many variations.
Add more layers, and each layer can learn progressively more sophisticated features:
Complex patterns with many layers
- Layer 1: Edges and textures
- Layer 2: Bark patterns and leaf shapes
- Layer 3: Branch structures
- Layer 4: Different tree species characteristics
- Layer 5: Trees in different seasons and conditions
Each layer builds understanding on top of the previous layer's discoveries. By the time you reach the output, the network can recognize oak trees in autumn, palm trees on beaches, pine trees in snow, and cherry blossoms in spring. All from those layered patterns.
The trade-off
More layers mean more calculations, more training time, and more examples needed. A shallow network might learn from 10,000 pictures. A deep network might need a million. But for complex tasks like understanding language, recognizing thousands of objects, or playing chess at master level, deep networks are worth the effort.
That's why ChatGPT and modern AI systems are "deep." They have dozens or hundreds of layers, learning incredibly complex patterns from massive amounts of text, images, or other data. Not because they're intelligent, but because the tasks they're doing require recognizing very subtle, very complex patterns.
The Two Approaches: Precision vs. Efficiency
Most neural networks today use very precise numbers for their calculations. When they multiply and add weights, they use numbers like 0.7234891 or 1.3982736. Lots of decimal places. Very precise.
The traditional approach: overkill precision
This is like measuring ingredients for a cake with a laboratory scale that's accurate to 0.001 grams. Flour: 247.384 grams. Sugar: 118.592 grams. Very precise, very accurate, and honestly, total overkill for baking a cake.
These precise numbers (called floating-point) require a lot of computational power. Each multiplication with these numbers takes thousands of transistor operations. When you're doing billions of multiplications to train a network, that adds up to enormous amounts of electricity and computing time.
The Binary Approach
Binary neural networks: just two values
There's another approach: binary neural networks. Instead of precise decimal numbers, use just two values. Positive one or negative one. That's it.
Sounds crazy, right? How can you learn complex patterns with just two numbers?
Grandmother's recipe approach
Think about it this way. When your grandmother baked that apple pie, she didn't use a laboratory scale. She used her eyes and experience. "About this much flour. A good handful of sugar. Butter about the size of an egg." Not precise at all, but the pie still turned out delicious.
Binary networks work similarly. Each connection is either "yes, this matters" (+1) or "no, this doesn't matter" (-1). No decimal precision. Just simple yes/no votes.
The magic is in the combination. Thousands of simple yes/no decisions combine to make surprisingly accurate predictions. Just like your grandmother's imprecise measurements combined with her experience to make a perfect pie.
The advantage
Binary networks run much faster and use far less energy. Remember from our earlier discussion about binary computing? Simple yes/no operations are hundreds of times faster than precise decimal math. A binary neural network can run on your phone, on a simple computer chip, even on tiny devices that don't have much power. The same network with precise numbers would need a massive computer and tons of electricity.
At Dweve, we focus on binary neural networks for real-world efficiency. Our Core system uses simple yes/no weights and runs 40 times faster than traditional networks while using 96% less energy. Not because we're cutting corners, but because for most real tasks, you don't need laboratory precision. Grandmother's recipe approach works just fine.
What Neural Networks Are Actually Good At
Now that you understand how neural networks work, let's talk about what they're actually useful for. Because despite all the hype, they're not good at everything.
Neural networks excel at pattern recognition. If you can show them thousands of examples with consistent patterns, they'll learn to recognize those patterns in new examples. That makes them perfect for:
📷 Recognizing images
Show the network a million labeled photos, and it learns to identify cats, dogs, cars, faces, tumors in X-rays, cracks in roads, whatever you trained it on. Your phone's camera uses this to focus on faces. Hospitals use it to spot diseases in medical scans.
🎤 Understanding speech
When you talk to Siri or Alexa, a neural network converts your voice patterns into text. It learned from thousands of hours of recorded speech what each sound pattern means. Different accents, background noise, mumbling… it handles all of it through pattern recognition.
💬 Processing language
Translation, answering questions, writing text. Networks trained on billions of words learn patterns in how language works. Not because they "understand" language, but because they recognize patterns like "this word usually follows that word" and "this sentence structure means a question."
🎬 Making recommendations
Netflix suggesting shows, Spotify finding music you'll like, Amazon recommending products. These networks learn patterns from millions of users. "People who liked A and B also liked C. This person likes A and B, so probably they'll like C too."
🎮 Playing games
Chess programs, Go players, video game AI. The network plays millions of games against itself, learning which moves lead to wins and which lead to losses. Pure pattern recognition through trial and error.
Notice the pattern? These are all tasks where consistent patterns exist in large amounts of data. Show a network enough examples, and it finds the patterns.
What Neural Networks Are Terrible At
Now for the reality check. Neural networks have serious limitations:
⚠️ They need massive amounts of data
A child sees three dogs and understands "dog." A neural network needs thousands or millions of dog pictures. No data? No learning. Small dataset? Poor performance. These systems are data-hungry monsters.
⚠️ They're black boxes
The network can't explain its decisions. It knows the answer (or thinks it does), but it can't tell you why. Millions of weights interacting in complex ways. No human can trace the reasoning. This is a big problem in medicine, law, and anywhere you need to justify decisions.
⚠️ They learn bias from data
If your training data has biases (and most real-world data does), the network learns those biases. Historical discrimination? The network will discriminate. Unbalanced examples? The network performs worse on underrepresented groups. Garbage in, garbage out.
⚠️ They don't generalize well
Train a network on cats and dogs, then show it a horse. It struggles. Humans generalize easily ("Oh, that's another four-legged animal"). Neural networks don't. They only know the specific patterns they saw during training. New situations confuse them.
⚠️ They can be fooled easily
Tiny changes invisible to humans can completely fool a network. Change a few pixels in a cat photo (changes you wouldn't even notice), and the network might suddenly say "Airplane!" with 99% confidence. This is called an adversarial attack, and it's a real security concern.
These aren't minor problems. They're fundamental to how neural networks work. A network is only as good as its training data, and it can only recognize patterns it has seen before.
The Real Bottom Line
So what are neural networks, really?
They're sophisticated pattern-matching machines. Multilayer switchboards that learn from examples. Mathematical optimization systems that adjust millions of tiny weights until predictions match reality.
They're not brains. They're not intelligent. They're not conscious. They don't "understand" anything. They recognize patterns through repetition and correction.
But within those limitations, they're remarkably powerful. They've transformed technology. Your phone's face recognition, voice assistants, spam filters, photo organization, navigation apps, streaming recommendations, automatic translations… all powered by neural networks.
The key is using them for the right jobs
Pattern recognition? Excellent. Tasks with clear examples and consistent patterns? Perfect. Creative problem-solving that requires understanding context and meaning? Not so much.
When someone tries to sell you on "AI" or "neural networks," ask yourself: Is this really a pattern recognition problem? Is there enough good data to train on? Will it work on situations it hasn't seen before? Can it explain its decisions when needed?
If the answers are yes, yes, probably, and no (respectively), then neural networks might be the right tool. If not, you might need something else.
The brain metaphor made neural networks sound exciting and mysterious. Understanding what they really are—sophisticated switchboards learning from examples—makes them less magical but far more useful. Because when you understand the tool, you know when to use it and when to reach for something else. And that understanding is worth more than any amount of marketing hype.
At Dweve, we build neural networks that respect reality. Binary operations. Efficient computation. Clear limitations. No magic, no hype, just honest engineering. Because the best AI is the kind that works in the real world, not just in research papers.
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About the Author
Marc Filipan
CTO & Co-Founder
Building the future of AI with binary neural networks and constraint-based reasoning. Passionate about making AI accessible, efficient, and truly intelligent.