Understanding Neural Network Neurons

Explaining What These Smart Components Doing?

This article aims to provide an overview of what the neurons within a neural network perform.

If you want to understand what neural networks are then please read:

Imagine this neural network

The artificial neural network shown above has 4 layers:

1. One Input layer
2. One Output layer
3. Two Hidden Layers

There are in total 10 neurons:

1. 2 input neurons
2. 6 hidden neurons — 3 neurons within each hidden layer
3. 2 output neurons

• Each neuron is connected with another neuron via synapses.
• Each neuron takes in an input from one-or-more neurons along with the weights and a bias which I will explain in detail later on.

What Is A Neuron?

Let’s take a look inside a neuron:

A neuron is a container that contains following key components:

1. A mathematical function which is known as an activation function
2. Inputs
3. A vector of weights
4. A bias

A neuron first computes the weighted sum of the inputs.

As an instance, if the inputs are:

And the weights are:

Then a weighted sum is computed as:

Subsequently, a bias (constant) is added to the weighted sum

Finally, the computed value is fed into the activation function, which then prepares an output.

Think of the activation function as a mathematical operation that normalises the input and produces an output. The output is then passed forward onto the neurons on the subsequent layer.

If you want to understand what weights and bias are then please read:

If you want to understand what activation functions are then please read:

If you want to understand what neural network layers are then please read:

Summary

This article provided an overview of neurons functions.

Hope it helps.