How does a McCulloch-Pitts neural net model the activity of neurons?

The McCulloch-Pitts neural net models the activity of neurons by representing them as logical components within a mathematical framework that mimics the "all or none" threshold behavior of biological neurons.1

In this model, time and excitation are quantized. A neuron $N$ with a defined threshold $h$ is considered "excited" at time $t$ if and only if:
* $h$ or more neurons with excitatory connections to $N$ were excited at time $t-1$.
* No neuron with an inhibitory connection to $N$ was excited at time $t-1$.1

If these conditions are not met, the neuron remains quiescent. This structure allows the net to process information through excitatory or inhibitory effects, where impulses travel in quantized form. The model includes input neurons, which represent sensory receptors, and output neurons, which record the final state of the configuration. By utilizing these logical schemata, the McCulloch-Pitts model provides a basis for understanding how nerve nets can store, codify, and use information, forming a foundational concept in automata theory.12