What is the key component in an artificial neural network that performs weighted summation and applies an activation function?

In the context of artificial neural networks, a neuron is the fundamental building block that performs both the weighted summation of inputs and applies an activation function. A neuron receives inputs, which are typically numerical values, and each input is associated with a weight that signifies its importance. The neuron computes the weighted sum of the inputs by multiplying each input by its corresponding weight and then adding them together. After calculating this sum, the neuron passes the result through an activation function, which introduces non-linearity into the model. This activation function determines the output of the neuron based on the weighted sum.

While the terms layer, node, and activation function are related, they do not directly define the component responsible for the weighted summation and activation. A layer consists of multiple neurons arranged in a specific configuration, a node can be an informal term sometimes used interchangeably with neuron, and the activation function itself is a mathematical operation rather than a component that encapsulates the weighted summation and activation process. Thus, identifying the neuron as the key component is essential to understanding how artificial neural networks function.