McCulloch-Pitts Model

#Artificial Neuron #Neural Network #Basics

The McCulloch-Pitts model maps the input $\{x_1, x_2,\cdots, x_i \cdots, x_N \}$ into a scalar $y\in\{1,-1\}$,

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

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.

Published: by ;

LM (2021). 'McCulloch-Pitts Model', Datumorphism, 02 April. Available at:

Current Ref:

  • cards/machine-learning/neural-networks/