Chapter 1

# McCulloch-Pitts Gate Explorer

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.
Threshold θ 2
Modified threshold
Computation
Truth Table