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: https://datumorphism.leima.is/cards/machine-learning/neural-networks/mcculloch-pitts-model/.

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