Understanding Neural Network Architecture: Neurons in a General Neural Network

Photo by Gustavo Boaron on Unsplash

Neurons, also referred to as nodes, are the basic computational units in a neural network. They are organized into layers, and each neuron processes information and performs computations on the input data. The architecture and functioning of neurons vary based on the type of neural network.

I. Neurons in a General Neural Network:

1. Input Layer Neurons:

• Neurons in the input layer represent individual features of the input data.
• Each neuron corresponds to a specific input feature, and the values of these neurons form the input vector.

Input Layer Neurons:
+---+---+---+---+
| x1| x2| x3| x4|
+---+---+---+---+

• In the example above, each neuron (x1,x2,x3,x4) represents a distinct feature of the input.
• The values of these neurons collectively form an input vector.
• The input vector is the numerical representation of the input data that is fed into the neural network.

Input Vector:
[x1, x2, x3, x4]

• Each neuron is responsible for encoding information about a specific aspect of the input.
• For example, in an image classification task, input neurons might represent pixel values or features extracted from an image.
• The number of neurons in the input layer is determined by the dimensionality of the input data.
• If the input data is a 3D vector, there would be three neurons; if it’s an image with pixel values, the number of neurons might be equal to the number of pixels.
• Input layer neurons might be subjected to normalization or preprocessing steps to ensure that the input data is standardized and suitable for the network.

2. Hidden Layer Neurons:

• Neurons in hidden layers perform computations on the input data.
• Each neuron in a hidden layer receives input from all neurons in the previous layer, and it produces an output based on a weighted sum of these inputs.
• Each neuron in a hidden layer receives input from all neurons…