McCulloch-Pitts Model

#Artificial Neuron #Neural Network #Basics

The McCulloch-Pitts model maps the input ${x_{1}, x_{2}, \dots, x_{i} \dots, x_{N}}$ into a scalar $y \in {1, - 1}$ ,

$y = sign (w \cdot x - b) .$

Since $w \cdot x - b = 0$ is a hyperplane, the McCulloch-Pitts model separates the state space using this hyperplane. The shift $b$ determines the interception, and $w$ decides the slope.

Planted: 2021-02-25 by L Ma;

References:

Vladimir N. Vapnik. 2000. The Nature of Statistical Learning Theory.

Dynamic Backlinks to cards/machine-learning/neural-networks/mcculloch-pitts-model:

Rosenblatt's Perceptron

Connected perceptrons

cards/machine-learning/neural-networks/mcculloch-pitts-model Links to:

Artificial Neural Networks

Simple artificial neural networks using multilayer perceptron

LM (2021). 'McCulloch-Pitts Model', Datumorphism, 02 April. Available at: https://datumorphism.leima.is/cards/machine-learning/neural-networks/mcculloch-pitts-model/.