**And now we are going to see What is Perceptron and we are going to learn the Mathematics behind this neuron in the simplest way.**

**So, Let’s Get Started.**

**First of All**, to Know about **Perceptron** you NEED to KNOW ” **What is Mcculloch Pitts Neuron?** “. My request to you to Read the blog post first, because, Mcculloch Pitts neuron and Perceptron are approximately Similar.

**Perceptron**

**Perceptron is a more general computational model than Mcculloch Pitts Neuron.**

**{X**are the

_{1, }X_{2, }X_{3, }…, X_{n}}**Inputs**and

**Y**is the

**Output**. And

**f**and

**g**are the

**Functions**. There are two types of inputs, One is

**Excitatory**Input, which is dependent and another is

**Inhibitory**Input, which is independent input. Here

**{X**are the Excitatory Inputs.

_{1, }X_{2, }X_{3, }…, X_{n}}**Here, (w**_{1}, w_{2}, w_{3}, …, w_{n} ) are the Weights.

_{1}, w

_{2}, w

_{3}, …, w

_{n}) are the Weights.

**The main difference between Mcculloch Pitts neuron and Perceptron is, an introduction of numerical weights (w**_{1}, w_{2}, w_{3}, … w_{n} ) for inputs and a mechanism for learning these weights.

_{1}, w

_{2}, w

_{3}, … w

_{n}) for inputs and a mechanism for learning these weights.