Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. While the computational implementations of ANNs relate to earlier discoveries in mathematics, their creation was inspired by biological neural circuitry. The first implementation of ANNs was the perceptron by Frank Rosenblatt. Despite the frequent claim that little research was conducted on ANNs in the 1970s and 1980s—the AAAI calling this period an "AI winter"—there was ongoing development of ANNs, especially connected to the development of specialized computers for simulating these networks. Later, advances in hardware and the development of the backpropagation algorithm, as well as recurrent neural networks and convolutional neural networks, renewed interest in ANNs. The 2010s saw the development of a deep neural network (i.e., one with many layers) called AlexNet. It greatly outperformed other image recognition models, and is thought to have launched the ongoing AI spring. The transformer architecture was first described in 2017 as a method to teach ANNs grammatical dependencies in language, and is the predominant architecture used by large language models such as GPT-4. Diffusion models were first described in 2015, and became the basis of image generation models such as DALL-E in the 2020s.
Mathematical foundations Jürgen Schmidhuber suggests that the first neural network was the method of linear regression by least squares, first published by Adrien-Marie Legendre in 1805 and independently developed by Friedrich Gauss (who claimed use since 1795) and Robert Adrain (1808), as it is mathematically equivalent to a two layer neural network without activation functions. The chain rule, developed by Gottfried Wilhelm Leibniz in 1676, and gradient descent, independently proposed by Augustin-Louis Cauchy in 1847 and Jacques Hadamard in 1907, are also central to the development of neural networks, as they form the basis of techniques for updating weights within complex networks.
Biological and computational models
Artificial neural network development of the 20th century was primarily based on research into the function of biological neurons. The neuron's place as the primary functional unit of the nervous system was first recognised in the late 19th century through the work of Santiago Ramón y Cajal, notably through his 1888 paper presenting staining of axons in the cerebellum of birds. Alexander Bain's Mind and Body (1873) proposed that thoughts and bodily activity result from neuronal processes, with each thought corresponding to a distinct neural grouping. William James's The Principles of Psychology (1890) advanced two principles on a quasi-neurological basis: first, that when two brain processes are active together, one tends to propagate excitement into the other, and second, that activity at any brain point is the sum of tendencies from all other points discharging into it. Warren McCulloch and Walter Pitts's 1943 paper "A Logical Calculus of the Ideas Immanent in Nervous Activity" studied several abstract models for neural networks, using the symbolic logic of Rudolf Carnap and Principia Mathematica. The paper argued that several abstract models of neural networks (some learning, some not) have the same computational power as Turing machines. This model paved the way for research to split into two approaches: one focused on biological processes, while the other focused on the application of neural networks to artificial intelligence. This also led to work on nerve networks and their link to finite automata. Some consider McCulloch and Pitts to be the founders of connectionism, a theory of mind in opposition to classical computationalism. In his 1948 report "Intelligent Machinery", published posthumously in 1969, Alan Turing proposed randomly connected networks of neuron-like nodes trainable through "education", defining A-type machines with random networks of NAND gates and B-type machines with modifiable connections. In 1949, the psychologist Donald O. Hebb published The Organization of Behavior, which proposed a learning hypothesis based on the mechanism of neural plasticity which became known as Hebbian learning, summarized as "neurons that fire together, wire together". Similar observations were made by Jerzy Konorski in 1948. The concept was used in many early neural networks, such as Rosenblatt's perceptron and the Hopfield network. This evolved into models for long-term potentiation. Belmont Farley and Wesley A. Clark (1954) were the first to use computational machines to simulate a Hebbian network. Other neural network computational machines were simulated by Nathaniel Rochester, John Holland, Lois Haibt and William Duda (1956). In 1959, a biological model was proposed by David H. Hubel and Torsten Wiesel based on their discovery of two types of cells in the primary visual cortex: simple cells and complex cells.
Perceptrons and other early neural networks
The perceptron was created by Frank Rosenblatt in 1957 while working at the Cornell Aeronautical Laboratory, publishing the details the following year. The perceptron was designed to classify objects into two categories, updating based on error feedback. He initially simulated the perceptron on an IBM 704, later designing the Mark I Perceptron, the first hardware neural net. In 1958, Rosenblatt proposed the multilayer perceptron (MLP) model, consisting of an input layer, a hidden non-learning layer with randomised weights, and an output layer with learnable connections. He published the book Principles of Neurodynamics in 1962, which also introduced variants and computer experiments, including a version (developed alongside Henry David Block and Bruce Knight) with four-layer perceptrons where the last two layers have learned weights. Bernard Widrow and his doctoral student Marcian Hoff developed ADALINE (Adaptive Linear Neuron) in 1960. Unlike Rosenblatt's perceptron, ADALINE adjusted weights based on their least mean squares (LMS) algorithm before applying the threshold function. MADALINE, the multilayer extension, was used to eliminate echo on phone lines, likely the first artificial neural network applied to a real‑world engineering problem.
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