What is a Neuron?

In the previous lesson, we learned that a Neural Network is an artificial intelligence model composed of multiple neurons.

In this lesson, we will explore the concept and role of a Neuron, the fundamental unit of a neural network.

Concept of a Neuron

A neuron is a structure designed based on the way nerve cells in the human brain exchange signals.

Just as neurons in the human brain send and receive signals through synapses, artificial neurons receive input values, perform computation, and generate output values.

An artificial neuron receives input values, processes them through specific computations, and generates outputs, optimizing the relationship between inputs and outputs through learning.

How a Neuron Works

A neuron operates through the following process:

1. Input

It accepts multiple input values $x_1, x_2, ..., x_n$.

2. Application of Weights

Each input value is multiplied by a weight $w_1, w_2, ..., w_n$ to adjust the importance of the input.

3. Summation

The weighted inputs are summed, and a bias $b$ is added.

$$z = (w_1 \cdot x_1) + (w_2 \cdot x_2) + ... + (w_n \cdot x_n) + b$$

4. Application of Activation Function

The sum $z$ is evaluated to determine if it surpasses a certain threshold, influencing the output value.

$$y = f(z)$$

Here, $f$ is an activation function, with commonly used functions including Sigmoid and ReLU.

We will cover each activation function in more detail in future lessons.

5. Output

The final calculated value $y$ is passed on to the next neuron.

Through this process, a neuron learns patterns from input data and generates outputs based on those patterns.

In the next lesson, we will explore how neurons come together to form a neural network.

Want to learn more?

Join CodeFriends Plus membership or enroll in a course to start your journey.

Learn moreSign In