Toggle inputs, adjust thresholds, and see how a single binary neuron computes logic gates.
The McCulloch-Pitts neuron (1943) is the simplest formal model of a nerve cell.
Each input carries a weight: excitatory (+1) or inhibitory (-1).
The neuron computes a weighted sum of its inputs and fires (outputs 1) if and only if
the sum meets or exceeds the threshold θ. By choosing different weights and thresholds,
a single M-P neuron can implement the classical logic gates: AND, OR, NOT, NAND, and NOR.
Controls
Gate Type
Inputs
x₁
0
x₂
0
Weights
Inhibitory weights: Negative weights act as inhibition. The neuron's output depends on whether the weighted sum Σ reaches threshold θ.