How Does an Artificial Neural Cell Work? (A Simple and Clear Explanation)
The artificial neural cell is the cornerstone of all modern AI systems, whether they generate text like GPT, draw images, or predict prices. Although its operation seems complex, it actually relies on simple and clear steps, all powered solely by numbers.
Let’s start from the beginning…
- Input A: How Does It Become Numbers?
Before data enters the neural network, it must be converted into numbers.
Important to know:
There is no single, universal index.
Each company (OpenAI, Google, Meta, etc.) has its own method for converting words or images into numbers.
This guide is called:
Embedding (Numerical Representation)
Quick example:
The word “shirt” is converted into numbers like:
[0.41, -1.22, 0.77, …]
An image is converted into numbers representing pixels
like:
[255, 128, 90, …]
Why does each company have its own guide?
Because each model learns this representation on its own during training.
It’s not created by humans; the network generates it automatically.
- Numbers Entering the Neuron
After the input is converted into numbers, these numbers enter the neuron.
The neuron contains three main elements:
a) Weights
Numbers that change during training and determine the “importance” of each input.
Example:
Price weight = 0.8
Shipping weight = 0.4
Marketing weight = 1.2
It’s as if each piece of information has a different “amplifier” that gives it more or less power. B) Bias
An additional number that helps the cell adjust the result.
Such as a small button that slightly raises or lowers the output.
C) Activation Function
A simple rule that determines the output shape.
Example: The ReLU function prevents the number from being negative:
If the output is < 0 → it becomes 0
- What exactly does the cell do? (Practical steps)
The neuron works in the following order:
Step 1: Multiply inputs by weights
Each input number is multiplied by its weight.
Step 2: Add results
All the multiplications are added together.
Step 3: Add bias
A simple number is added to adjust the value.
Step 4: Pass the result through the activation function
The result is “cleaned” by the activation function.
The final equation for the cell:
\text{Output} = \text{Activation}(W \cdot X + b)
Where:
X = Input
W = Weights
b = Bias
Activation = Activation Function
- What happens after numbers are output from the cell?
If it’s a large neural network, these numbers go to:
Another cell
Another layer
Or a group of cells
Until entire “paths” of computations are built.
Networks consist of:
Input layers
Hidden layers
Output layers
Each layer contains many neurons.
- How are the final numbers converted to output B?
Output B is the final destination of the neural network:
If the system is generating text:
The final numbers are converted into a word.
Example:
[1.9, -0.2, 3.1, …] → “Hello”
If it generates an image:
The last digits are converted to:
The color of each pixel
Then they are combined into an image
If it predicts a price:
The last digits are:
Prediction = $24.7
Creative Representation (of Memory)
Imagine a neuron as a small chef:
1. Receives ingredients (Input A) → but they must be numbers.
2. Each ingredient has a spoon of a different size (Weight).
3. Mixes the ingredients (Addition).
4. Adjusts the flavor with a simple touch (Bias).
5. Passes the dish through a strainer (Activation Function).
6. Serves the final dish (Output B).
And so, millions of neurons work together to produce:
Texts
Images
Translations
Predictions
Anything else
The Golden Rule
Every AI system in the world operates with the same basic steps:
1. Input A → is converted into numbers (embedding specific to each company).
2. The numbers enter neurons (Weights + Bias + Activation).
3. The numbers are passed through thousands of pathways.
4. The final numbers are output → are converted into output B (word, image, prediction…).