Artificial Neural Network v/s Biological Neural Network

This is my first ever article and am taking immense pleasure in writing this article.

In this article we are going to look at the basic understanding of Artificial Neural Network (ANN)and Biological Neural Network and the basic structure of ANN’s and how it works as Biological Neural Network.

First we will see what is ANN and BNN:

Biological Neural Network:

The neural system of the human body consists of three stages:

a) receptors: The receptors receive the stimuli either internally or from the external world, then pass the information into the neurons in a form of electrical impulses

b) neural network : The neural network then processes the inputs then makes proper decision of outputs

3) effectors : the effectors translate electrical impulses from the neural network into responses to the outside environment.

Artificial Neural Network:

a) input layer: brings the initial data into the system for further processing by subsequent layers of artificial neurons.

b) hidden layer: a layer in between input layers and output layers, where artificial neurons take in a set of weighted inputs and produce an output through an activation function

c) output layer: the last layer of neurons that produces given outputs for the program.

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Structure of ANN and BNN:

Now lets see how ANN works as BNN:

An artificial neuron that receives a signal then processes it and can signal neurons connected to it. The “signal” at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs.

The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection.

Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers.

Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), hidden layer to the last layer (the output layer), possibly after traversing the layers multiple times.

What are the other functions ANN’s perform are:

• Ability to learn: Neural networks has ability to learn on their own and determines their function based only on inputs
• Ability to generalize: Human brain has ability to generalize things, ANN has also adapted the same characteristics.
• Adaptivity: ANN can readjust to changes or variances in data.

I hope you find my article helpful for you to understand the basic functionality of Artificial Neural Network and how it works as Biological Neural Network.

#Keeplearning#keepGrowing#MyANN#ANNv/sBNN#MachineLearning#Mylearning#greatlearning#PowerAhead.

Thank You.