"Boss" of a Neuron

Imagine an office department. Employees process incoming information — these employees represent the weights of a neuron, and the information they receive is the input. After the employees finish their work, the head of the department decides what to do next. In this analogy, the head is the activation function.

Each weight (employee) handles information differently, but the final decision is made by the activation function — the neuron’s internal “boss.” It evaluates the processed value and decides whether to send this signal forward or suppress it. This helps the network pass along only the most relevant information.

Mathematically, an activation function introduces non-linearity, allowing neurons to detect complex patterns that linear functions cannot capture. Without non-linear activation functions, a neural network would behave like a simple linear model, no matter how many layers it has.

Activation Function Options

Neural networks commonly use the following activation functions:

• Sigmoid: maps any real number into the range 0 to 1. Useful when the output represents a probability or degree of certainty;

• ReLU (Rectified Linear Unit): outputs 0 for negative values and keeps positive values unchanged. ReLU is simple, efficient, and helps networks learn complex patterns without the vanishing-gradient issue common in sigmoid/tanh;

• Tanh (Hyperbolic Tangent): similar to sigmoid but outputs between –1 and 1, giving it a stronger gradient for negative inputs and often making it more effective than sigmoid in hidden layers;

Activation Function Differences

Different activation functions are used in different cases, depending on what task the neural network needs to solve.

If the ReLU activation function is used, the neuron operates on a simple rule — it keeps all important (positive) values and discards all unimportant (negative) ones.

When a neuron uses a sigmoid activation, its output becomes a value between 0 and 1, interpretable as a probability or importance score. This helps the network decide how strongly the neuron should influence the next layer.

Overall, the activation function is the rule that determines how a neuron reacts to incoming information. It adds flexibility, shapes how signals flow through the network, and allows the model to learn rich, layered patterns — ultimately making neural networks capable of accurate and adaptive predictions.