Artificial Neural Networks
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Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
An artificial neuron that receives a signal then processes it and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges.
An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
An artificial neuron that receives a signal then processes it and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges.
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Warren McCulloch and Walter Pitts (1943) opened the subject by creating a computational model for neural networks. In the late 1940s, D. O. Hebb created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning.
Farley and Wesley A. Clark (1954) first used computational machines, then called "calculators", to simulate a Hebbian network. Rosenblatt (1958) created the perceptron. The first functional networks with many layers were published by Ivakhnenko and Lapa in 1965, as the Group Method of Data Handling. |
The basics of continuous back-propagation were derived in the context of control theory by Kelley in 1960 and by Bryson in 1961, using principles of dynamic programming. Thereafter research stagnated following Minsky and Papert (1969), who discovered that basic perceptrons were incapable of processing the exclusive-or circuit and that computers lacked sufficient power to process useful neural networks.
In 1970, Seppo Linnainmaa published the general method for automatic differentiation (AD) of discrete connected networks of nested differentiable functions. |
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In 1973, Dreyfus used backpropagation to adapt parameters of controllers in proportion to error gradients. Werbos's (1975) backpropagation algorithm enabled practical training of multi-layer networks. In 1982, he applied Linnainmaa's AD method to neural networks in the way that became widely used. The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of Complementary MOS (CMOS) technology, enabled increasing MOS transistor counts in digital electronics.
This provided more processing power for the development of practical artificial neural networks in the 1980s. In 1986 Rumelhart, Hinton and Williams showed that backpropagation learned interesting internal representations of words as feature vectors when trained to predict the next word in a sequence. In 1992, max-pooling was introduced to help with least-shift invariance and tolerance to deformation to aid 3D object recognition.
This provided more processing power for the development of practical artificial neural networks in the 1980s. In 1986 Rumelhart, Hinton and Williams showed that backpropagation learned interesting internal representations of words as feature vectors when trained to predict the next word in a sequence. In 1992, max-pooling was introduced to help with least-shift invariance and tolerance to deformation to aid 3D object recognition.
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