You know what annoys me most in conversations about artificial intelligence? When someone smugly declares: “Well, once a neural network has as many neurons as a human, we'll get a real AI.” Usually, after that, I take a deep breath and start explaining why that's like comparing the number of bricks in a house to the number of cells in an organism. Yes, both have basic elements. But that's where the similarity ends.
Let's figure out what's really going on with these “neurons” and why AI marketers love to juggle numbers.
The Biological Neuron: When Nature Puts on a Masterclass
Let's start with the fact that a real neuron in your brain isn't just a switch. It's an entire biochemical factory that operates on principles that would make any programmer's head spin.
Imagine a cell ranging from ten to a hundred micrometers in size. It has a body (soma), dendrites – these branching extensions for receiving signals – and an axon, a long tail for transmitting information. And this axon can stretch up to a meter in length. Yes, a single cell can be as long as your leg. Interesting already, isn't it?
But the real fun begins when a neuron decides to transmit a signal. It's not just an electrical impulse down a wire. It's an incredibly complex electrochemical process involving:
- Ion channels that open and close depending on voltage
- Sodium, potassium, calcium, and chlorine ions rushing back and forth across the membrane
- Neurotransmitters – chemical substances, of which there are over a hundred different types
- Synaptic vesicles that release these neurotransmitters into the synaptic cleft
- Receptors on the receiving end that react to these substances
And I haven't even mentioned astrocytes, glial cells, the myelin sheath, and a dozen other members of this biological orchestra. A single neuron can have up to ten thousand synaptic connections with other neurons. Ten thousand! And each connection is unique and can change in strength depending on activity.
And here's the kicker: a neuron doesn't just add up input signals and output a one or a zero. It considers the timing of the signals, their sequence, their frequency, and the type of neurotransmitter. It can be excitatory or inhibitory. It can modulate its own sensitivity. It can even generate spontaneous impulses without any external input.
Essentially, a single biological neuron is already a small neural network in itself. And you have about eighty-six billion of them in your brain.
The Artificial Neuron: An Elegant Mathematical Abstraction
Now, let's move on to what we proudly call an “artificial neuron.” Prepare for disappointment.
An artificial neuron in a neural network is a function. Just a mathematical function. It takes several input values, multiplies each by its weight coefficient, sums them all up, adds a bias, and passes the result through an activation function. That's it. You could literally write it in a single line of code.
It looks something like this: you take input data x₁, x₂, x₃, and so on, multiply them by weights w₁, w₂, w₃, sum them up, add a constant b (the bias), and pass the result through a function like sigmoid or ReLU. Mathematically, this is written as y = f(w₁x₁ + w₂x₂ + ... + b), where f is your activation function.
Yes, there are different types of artificial neurons. There are perceptrons, neurons in convolutional networks, and LSTM cells in recurrent networks. But the essence is the same: they are mathematical operations on numbers. No biochemistry, no ions, no neurotransmitters.
When you train a neural network, you are simply adjusting these weights and biases so that the output resembles the correct answer. This is loss function optimization via gradient descent. It's beautiful, it's elegant, but comparing it to a biological neuron is like comparing a calculator to a human.
Comparing the Incomparable: Where They Are Actually Similar
Okay, I might have exaggerated a bit. Artificial neurons were, after all, inspired by biological ones, and they do have some things in common.
First, the idea of summing weighted input signals is vaguely reminiscent of how a biological neuron integrates postsynaptic potentials. When dendrites receive signals from other neurons, these signals are summed up in the cell body, and if the total excitation exceeds a threshold, the neuron “fires” – it generates an action potential.
Second, both have the concept of an activation threshold. In a biological neuron, it's the membrane potential that must reach a certain value. In an artificial one, it's the activation function that decides how strongly the neuron should “respond” to the input signal.
Third, both systems are capable of learning by changing the strength of their connections. In the brain, this is called synaptic plasticity – synapses can be strengthened or weakened depending on activity. In neural networks, we change the connection weights through backpropagation of error.
But here's the important thing to understand: these similarities are so superficial that using them to draw serious conclusions is like judging an internal combustion engine by looking at a steam engine because “they both run on fuel.”
Key Differences: Why More Isn't Better
Now let's talk about why you can't just throw a billion artificial neurons together and get something resembling a brain.
Temporal Dynamics
Biological neurons treat time as a key parameter. Impulses arrive at specific moments, and their temporal sequence matters. This is called spike-based encoding. Two impulses arriving five milliseconds apart can mean something completely different from two impulses arriving fifty milliseconds apart.
Classic artificial neurons work with static values. You feed numbers in, you get numbers out. There are no temporal dynamics. Yes, recurrent networks and spiking neural networks exist and try to account for this, but even they are simplified models of the brain's real temporal dynamics.
Energy Efficiency
The human brain consumes about twenty watts of energy. Twenty! That's less than a light bulb. Yet it performs computations that require megawatts for modern supercomputers.
The large language models we're using in early 2026 require entire data centers to run. Training one large model can consume as much electricity as a small city does in a month. And that's with the number of parameters in these models still being less than the number of synapses in the brain.
Architecture and Connectivity
Artificial neural networks typically use a layered architecture: there's an input layer, hidden layers, and an output layer. Information flows in one direction (or with some feedback loops in recurrent networks).
The brain isn't made of layers. It's an incredibly complex three-dimensional structure with feedback loops, parallel processing pathways, and modular subsystems. Information is processed simultaneously in dozens of brain regions that are constantly exchanging signals with each other. And this architecture has been shaped by millions of years of evolution for specific survival tasks.
Multitasking and Generalization
A person can learn to drive a car, and these skills will help them learn to ride a motorcycle or even fly a plane more quickly. We transfer knowledge between tasks, generalize, and find analogies.
An artificial neural network trained to classify images of cats and dogs is completely helpless when faced with the task of predicting stock prices. It needs to be retrained from scratch. Yes, there are approaches like transfer learning and multimodal models, but they are still a long way from human flexibility.
Chemical Modulation
The brain has neurotransmitters and hormones that globally affect the operation of neural ensembles. Dopamine, serotonin, norepinephrine – these substances can change the sensitivity of entire brain regions, affecting attention, motivation, and mood.
There's nothing like that in artificial networks. We have hyperparameters that we set before training, but that's not the same as dynamic chemical regulation.
Why Quantity Doesn't Translate into Quality
Now, to the main question: why can't we just make a huge neural network with eighty-six billion neurons and get an artificial brain?
Because the problem isn't with the quantity. The problem is with the quality of the elements themselves and how they are organized.
Imagine you're trying to reproduce a Beethoven symphony using identical toy flutes. You could use a thousand flutes, ten thousand, a million – you still wouldn't get a symphony. Because a toy flute is not a violin, not a cello, not a bassoon. It has a different range, a different timbre, different capabilities.
It's the same with neurons. An artificial neuron is not a simplified version of a biological one. It's a completely different entity that performs a much more primitive function.
As I mentioned, a single biological neuron itself possesses computational power comparable to a small neural network. It processes information in multiple dimensions: electrical, chemical, temporal. It adapts, modulates, and interacts with its environment in complex ways.
If we truly wanted to model the human brain's operation at a biological level, we would need to simulate not just eighty-six billion “nodes,” but the entire biochemistry of each neuron, all its ion channels, all its synaptic vesicles, all the dynamics of its neurotransmitters. By some estimates, this would require an exaflop of computational power for every square millimeter of cortex. And that's just for a low-level simulation, with no guarantee that we even understand all the mechanisms correctly.
So What if We Make Artificial Neurons More Complex?
To be fair, researchers understand this. And in recent years, more and more work has emerged that tries to make artificial neurons more “biological.”
Spiking neural networks attempt to account for the temporal dynamics of impulses. Instead of constant values, their neurons generate spikes at specific moments, and information is encoded in the frequency and timing of these spikes.
Dendritic computing is an approach that tries to model the complex signal processing in the dendritic tree. It turns out that dendrites are not just passive wires but active computational elements.
Neuromorphic chips, like Intel's Loihi or IBM's TrueNorth, attempt to reproduce some of the operating principles of biological neurons at the hardware level. They are more energy-efficient and can process information in parallel, like the brain.
But even these approaches are still huge simplifications. We take certain principles of brain function and try to engineer them. It's better than classic neural networks, but there's still a huge gap to a real biological neuron.
So Why Call Them Neurons at All?
Good question. I think it's a historical accident. When Warren McCulloch and Walter Pitts proposed the first mathematical model of a neuron in 1943, they were genuinely inspired by biology. Their model was an attempt to understand how the brain might work.
But since then, artificial neural networks have gone their own way. They became a tool for solving practical problems: pattern recognition, natural language processing, playing chess. And it turned out that for these tasks, you don't need to copy biology precisely. It's enough to take the general idea of parallel information processing with trainable weights.
The name “neuron” has stuck around more as a nod to tradition and a convenient metaphor. It helps explain the concept to people far from mathematics: “Imagine it's like neurons in the brain, but simplified.”
The problem is that this metaphor creates false expectations. People hear that GPT-4 or GPT-5 has hundreds of billions of parameters and think, “Wow, that's almost like a brain!” No, it's nothing like a brain. It's a huge statistical model that has learned to predict the next word in a text with impressive accuracy. But it doesn't understand meaning in the way that we do.
What This Means for the Future of AI
Does all this mean we will never create a true artificial intelligence comparable to a human? No, it doesn't.
But it does mean the path there is much more complex than just “let's make the network bigger.” We need qualitatively new architectures, new principles of information processing, and perhaps a new understanding of what intelligence even is.
Interestingly, modern large language models demonstrate emergent abilities – properties that unexpectedly appear as the scale increases. The models start solving tasks they weren't explicitly trained on. This is a hint that quantity can sometimes translate into quality. But that doesn't make their operating mechanisms similar to the human brain.
There's also a hypothesis that to create a truly intelligent AI, we might need to reproduce not just neurons, but the process of evolution and development. The brain isn't formed according to a predefined blueprint but through a complex interaction of genes, environment, and chance. Maybe a true AI also needs to be “grown,” not engineered.
Or, perhaps, we don't need to copy biology at all. An airplane doesn't flap its wings like a bird, but it flies. A computer doesn't use neurons, but it computes. Maybe artificial intelligence will find its own path to sentience, one that is completely unlike our own.
The Practical Takeaway for Those Who've Read This Far
If you work with neural networks or are just interested in the topic, it's important to understand this difference. When someone starts comparing the number of parameters in a model to the number of neurons in the brain, it's a red flag. It's either a misunderstanding of the topic or a deliberate attempt to mislead.
Artificial neural networks are a powerful tool. They solve problems that seemed like science fiction just a decade ago. But they operate on their own principles, which are radically different from how the brain works.
Calling a node in a neural network a neuron is technically incorrect, but it's an established term, and fighting it is pointless. The main thing is to understand what actually lies behind that term. It's not a biological cell, not a tiny brain, not an element of consciousness. It's a mathematical function with adjustable parameters.
So the next time you hear that “a neural network with a trillion neurons will be smarter than a human,” remember the bricks and the cells. The number of building blocks doesn't determine the quality of the structure. What matters is what those blocks are, how they're connected, and what function they perform.
The brain is the result of billions of years of evolution, an incredibly complex, self-organizing, adaptive system. We are only just beginning to understand how it works. And while artificial neural networks are inspired by the brain, they are as far from it as a paper airplane is from a Boeing. They both fly, but that's where the similarity ends.
So the next time you read another article about a “neural network that works like the human brain,” apply a healthy dose of skepticism. It probably doesn't work like the brain at all. And that's okay. It's just different.
