How are neurons in a multi-layer perceptron (MLP) hidden layer arranged?

In a multi-layer perceptron (MLP), the arrangement of neurons in a hidden layer facilitates the processing of input data through to the output layer. Each neuron in a hidden layer typically connects to all neurons in the subsequent layer, which in this case would be the output layer. This full connectivity allows the hidden layer to capture a wide range of patterns and interactions in the data, contributing to the overall efficacy of the model.

When every neuron in one layer is connected to every neuron in the next layer, it enables the network to learn complex representations by combining the outputs from the hidden layer neurons. This characteristic is particularly essential for tasks that require a deep understanding of features and patterns, such as image recognition or natural language processing.

The other options suggest more restricted or specific connections that do not accurately describe the standard structure of MLPs. In practice, each neuron typically isn't limited to connecting to just one specific output neuron or selectively connecting to a subset of them, unless a specific architecture is designed with these restrictions in mind. The comprehensive interconnection pattern among the neurons allows for greater flexibility and capacity in how the network processes information and learns from data.