The most fundamental architecture for an artificial neural network (ANN) that can learn non-linear patterns is the Multi-Layer Perceptron (MLP). This structure minimally consists of three types of layers. The input layer receives the raw data or features. At least one hidden layer processes the inputs through a series of weighted connections and activation functions, enabling the network to learn complex relationships. Finally, the output layer produces the final prediction or classification. This input-hidden-output sequence forms the simplest complete feedforward neural network.

A: Pooling layers are specialized components used for down-sampling, primarily in Convolutional Neural Networks (CNNs), and are not a fundamental requirement for a basic ANN.

B: Convolutional layers are also specialized, designed for feature extraction from grid-like data (e.g., images), and are not part of a minimal, general-purpose ANN.

D: A dropout layer is a regularization technique to prevent overfitting, not a core computational layer. This option also critically omits the essential output layer.

1. Goodfellow

I.

Bengio

Y.

& Courville

A. (2016). Deep Learning. MIT Press. In Chapter 6

"Deep Feedforward Networks

" Section 6.1 describes the basic model as consisting of an input layer

one or more hidden layers

and an output layer. The simplest non-trivial example shown requires all three.

2. Nielsen

M. A. (2015). Neural Networks and Deep Learning. Determination Press. Chapter 1

"Using neural nets to recognize handwritten digits

" introduces the standard network architecture: "The leftmost layer... is called the input layer... The rightmost or output layer... The middle layer is called a hidden layer."

3. Stanford University. (n.d.). CS231n: Convolutional Neural Networks for Visual Recognition - Module 1: Neural Networks Part 1: Setting up the Architecture. The course notes define a "2-layer Neural Network" as having one hidden layer

which implies the full structure of an input layer

one hidden layer

and an output layer.