a nonlinear threshold logic unit that decides whether an input belongs to one of two classes

a simple kind of neural network

is a feedforward network with no hidden neurons.

An early model for the processing units that may be used in neural networks. Perceptrons are noted for the simplicity of the function they perform on input.

A large class of simple neuron-like networks with only an input layer and an output layer. Developed in 1957 by Frank Rosenblatt, this class of neural network had no hidden layer.

An artificial neural network capable of simple pattern recognition and classification tasks. It is composed of three layers where signals only pass forward from nodes in the input layer to nodes in the hidden layer and finally out to the output layer. There are no connections within a layer.

The simplest type of feedforward neural network. It has only inputs and outputs, i.e., no hidden layers.

The perceptron is a type of artificial neural network invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt. It can be seen as the simplest kind of feedforward neural network: a linear classifier.