From Human Neuron to Perceptron Model

Human neurons are the basic unit of the brain, a highly complex, interconnected network responsible for processing and transmitting information using electrical and chemical signals. Just like neurons, the perceptron model plays a similar role in terms of machine learning models. The journey from understanding human neurons to developing the perceptron model marks a significant milestone in the field of machine learning and deep learning. It reflects how biological structure has led to the development of computational models that capable of learning and making decisions.

Human neuron

Simplified biological neuron model

The mimic mathmatical model

Inputs(x1, x2) represent features of the dataset.

Add weights(w) to learn

Summation and activation: The weighted inputs are summed, and an activation function is applied to the sum.

Learning process

The perceptron learns by adjusting the weights based on the difference between predicted values and real value. This process is akin to strenthening or weakening passes in the human brain.

The perceptron model laid the groundwork for artificial neural networks. While it represents a simplified version of biological neural processing, its conceptualization has been pivotal in advancing the field of artificial intelligence, leading to more sophisticated models capable of tacking a wider range of complex tasks.