3.1 MP Neurons

Question 1

What is the formula for McCulloch-Pitts neurons

Answer 1

Question 2

How does the MP neuron relate to the biology

Answer 2

Question 3

Does the perceptron and hopfied network use MP neurons as a base

Answer 3

Yes

Question 4

How do perceptrons differ from MP neurons

Answer 4

Perceptrons and McCulloch-Pitts (MP) neurons are both foundational models in artificial neural networks but differ primarily in functionality and learning capabilities:

*	MP Neurons are basic binary threshold units developed to mimic neuron firing behavior. They apply a weighted sum to inputs and activate (output 1) if the sum exceeds a threshold; otherwise, they remain inactive (output -1). They are static and don’t learn.
*	Perceptrons expand on MP neurons by adding a learning algorithm. They adjust weights iteratively to minimize errors in binary classification tasks, enabling them to learn linearly separable patterns through training.

In essence, perceptrons are trainable versions of MP neurons, enhancing adaptability in network learning.