Single-Layer Neural Network

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What Does Single-Layer Neural Network Mean?

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.

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Techopedia Explains Single-Layer Neural Network

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.

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Margaret Rouse
Technology Expert
Margaret Rouse
Technology Expert

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.