Binary Neuron

The Binary rule models a neuron that switches between two discrete values based on a threshold comparison. This is a hard-threshold activation function commonly used in early neural models and logic-based systems.

At each time step, the neuron’s input is compared to a threshold:

\[a = \begin{cases} \text{upperBound}, & \text{if } x > \text{threshold} \\ \text{lowerBound}, & \text{otherwise} \end{cases}\]

Where:

• \(x\) is the input to the neuron,
• \(a\) is the resulting activation.

This rule is often used for binary classification, digital logic emulation, and models where sharp decision boundaries are needed.

Parameters

• Threshold: The input value above which the neuron fires (i.e., switches to the upper bound).

For all other parameters, see common neuron properties