Multi-Layer Perceptrons (MLPs) in neural networks consist of interconnected nodes that perform simple mathematical operations, revealing complexity in how they compute results.
MLPs can be used to approximate equations and discover underlying patterns in experimental data, but may not efficiently solve known mathematical functions unless they memorize data.
Analyzing MLP parameters can reveal insights, improve model training, and potentially lead to the discovery of unknown equations or constants in scientific research.