Artificial Neural Networks/History
The history of neural networking arguably started in the late 1800s with scientific attempts to study the workings of the human brain. In 1890, William James published the first work about brain activity patterns. In 1943, McCulloch and Pitts produced a model of the neuron that is still used today in artificial neural networking. This model is broken into two parts: a summation over weighted inputs and an output function of the sum.
Artificial Neural Networking
In 1949, Donald Hebb published The Organization of Behavior, which outlined a law for synaptic neuron learning. This law, later known as Hebbian Learning in honor of Donald Hebb is one of the simplest and most straight-forward learning rules for artificial neural networks.
In 1951, Marvin Minsky created the first ANN while working at Princeton.
In 1958 The Computer and the Brain was published posthumously, a year after John von Neumann’s death. In that book, von Neumann proposed many radical changes to the way in which researchers had been modeling the brain.
Mark I Perceptron
The Mark I Perceptron was also created in 1958, at Cornell University by Frank Rosenblatt. The Perceptron was an attempt to use neural network techniques for character recognition. The Mark I Perceptron was a linear system, and was useful for solving problems where the input classes were linearly separable in the input space. In 1960, Rosenblatt published the book Principles of Neurodynamics, containing much of his research and ideas about modeling the brain.
The Perceptron was a linear system with a simple input-output relationship defined as a McCulloch-Pitts neuron with a step activation function. In this model, the weighted inputs were compared to a threshold θ. The output, y, was defined as a simple step function:
Despite the early success of the Perceptron and artificial neural network research, there were many people who felt that there was limited promise in these techniques. Among these were Marvin Minsky and Seymore Papert, whose 1969 book Perceptrons was used to discredit ANN research and focus attention on the apparent limitations of ANN work. One of the limitations that Minsky and Papert pointed out most clearly was the fact that the Perceptron was not able to classify patterns that are not linearly separable in the input space. Below, the figure on the left shows an input space with a linearly separable classification problem. The figure on the right, in contrast, shows an input space where the classifications are not linearly separable.
Despite the failure of the Mark I Perceptron to handle non-linearly separable data, it was not an inherent failure of the technology, but a matter of scale. The Mark I was a two layer Perceptron, Hecht-Nielsen showed in 1990 that a three layer machine (multi layer Perceptron, or MLP) was capable of solving nonlinear separation problems. Perceptrons ushered in what some call the “quiet years”, where ANN research was at a minimum of interest. It wasn’t until the rediscovery of the backpropagation algorithm in 1986 that the field gained widespread interest again.
Backpropagation and Rebirth
The backpropagation algorithm, originally discovered by Werbos in 1974 was rediscovered in 1986 with the book Learning Internal Representation by Error Propagation by Rumelhart, Hinton and Williams. Backpropagation is a form of the gradient descent algorithm used with artificial neural networks for minimization and curve-fitting.
In 1987 the IEEE annual international ANN conference was started for ANN researchers. In 1987 the International Neural Network Society (INNS) was formed, along with the INNS Neural Networking journal in 1988.