What is Single Artificial Neuron?

A single artificial neuron is the basic computational unit of a neural network.
It receives input features, multiplies them with weights, adds a bias, and passes the result through an activation function to produce an output.
The output may be a probability or a continuous value depending on the activation function used.

Examples of activation functions: sigmoid, ReLU, tanh, step

What is Perceptron?

A perceptron is the simplest type of artificial neuron that uses a step (threshold) activation function to classify inputs into one of two classes.
It produces a binary output (0 or 1) based on whether the weighted sum exceeds a threshold.

It is mainly used for linearly separable binary classification problems.

Feature	Single Artificial Neuron	Perceptron
General concept	Broad term for one neuron	Specific type of neuron
Activation function	Can be sigmoid, ReLU, tanh, step	Only step (threshold)
Output type	Continuous / probability	Binary (0 or 1)
Learning	Smooth learning using gradients	Hard decision learning
Error reduction	Gradual	Only when misclassified
Used in deep learning	Yes	No
Example output	0.668, 0.72	0 or 1