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A single-layer neural network represents the most simple form of neural network, in which there is only one layer of input nodes that send weighted inputs to a subsequent layer of receiving nodes, or in some cases, one receiving node. This single-layer design was part of the foundation for systems which have now become much more complex.
One of the early examples of a single-layer neural network was called a “perceptron.” The perceptron would return a function based on inputs, again, based on single neurons in the physiology of the human brain. In some senses, perceptron models are much like “logic gates” fulfilling individual functions: A perceptron will either send a signal, or not, based on the weighted inputs. Another type of single-layer neural network is the single-layer binary linear classifier, which can isolate inputs into one of two categories.
Single-layer neural networks can also be thought of as part of a class of feedforward neural networks, where information only travels in one direction, through the inputs, to the output. Again, this defines these simple networks in contrast to immensely more complicated systems, such as those that use backpropagation or gradient descent to function.