Artificial neural networks mimic human brain function in a loose and useful way. That is the right starting point, and also the limit. They are built from connected units that take inputs, weight them, and pass along outputs, which is a simple software version of how biological neurons pass signals.
I keep coming back to one fact: the match is about function, not biology. A real brain has living cells, chemical signals, and a huge web of timing effects. An artificial neural network has numbers, math, and code. The name is honest enough to point at the idea, but it can also mislead people into thinking the model is a small digital brain. It is not.
What an artificial neural network does well is learn patterns from data. Each unit, often called a neuron, gets input values. It combines them with weights, then sends the result through an activation function. That activation is a small rule that decides how strongly the unit should respond. With many units stacked together, the network can learn useful patterns like image labels, speech patterns, or text relations.
That design is inspired by the brain, but it is a stripped-down version. Biological neurons do far more than add numbers and apply a simple formula. They fire in complex ways, use many kinds of chemistry, and work with timing that current standard neural networks do not model well. The network architecture we use in machine learning borrows the broad idea of connected processing units, then turns it into mathematics that a computer can train.
Training is the part that makes the system feel intelligent. The network makes a guess, checks how wrong it was, and adjusts its weights a little. It repeats this many times. Over time, the network learns which input patterns matter. That is one of the main reasons these models are useful. They do not need a programmer to hand-write every rule.
Still, this is where the honest limit matters. Artificial neural networks do not think the way people think. They do not understand in a human sense. They do not have memory, awareness, or intent like a brain does. They are good at pattern fitting, not at being a mind. Even when their behavior looks smart, the internal process is still a mathematical one, not a biological one.
I think this is the point many readers need most. The brain inspired the design, but the current algorithm is not a brain copy. It is a practical model that uses the loose lesson of the brain: many small units working together can solve hard tasks. That idea turned out to be powerful, even if the machine version is far simpler than the real thing.
There is also a useful split between older claims and current reality. Early descriptions often made the comparison sound closer than it is. Modern work is more careful. Some neural network ideas do resemble parts of brain behavior in broad form. But the full biological system remains much richer than what most artificial networks do today. Spiking neural networks and other brain-inspired models try to close that gap, yet they are still not the same as the human brain.
So the short answer stays the same. Artificial neural networks mimic human brain function by copying the broad idea of connected units that learn from signals. They do not copy the brain itself. They are engineered tools, built for pattern learning, and they work because the simplified idea is useful, not because they truly reproduce human thought.
That is the kind of plain answer I want from an AI concept: one clear idea, one working mechanism, and one hard limit. That is also the promise behind The Model Log, with one practical AI concept, one working example, and one honest look at what actually works.
Related: Sztuczne sieci neuronowe imitują funkcje ludzkiego mózgu
