Oja

Oja's rule is a mathematical principle used in neural networks to help the system learn which inputs are most important. Imagine a network that tries to find patterns in data; Oja's rule adjusts the weights (connections) gradually based on the input and output signals, ensuring the strongest input signals become more influential over time. It helps the network focus on the most relevant features without the weights growing too large or unstable. Essentially, Oja's rule is a way for models to learn in a stable, self-regulating manner, improving their ability to recognize key patterns in complex data.