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CT: Perceptrons are the easiest data structures to learn for the study of Neural Networking. Think of a perceptron as a node of a vast, interconnected network, sort of like a data tree, although the network does not necessarily have to have a top and bottom. The links between the nodes not only show the relationship between the nodes but also transmit data and information, called a signal or impulse. The perceptron is a simple model of a neuron (nerve cell).
Since linking perceptrons into a network is a bit complicated, let’s take a perceptron by itself. A perceptron has a number of external input links, one internal input (called a bias), a threshhold, and one output link. To the right, you can see a picture of a simple perceptron. It resembles a neuron.
Usually, the input values are boolean (that is, they can only have two possible values: on and off, 1 or 0, true or false), but they can be any real number. The output of the perceptron, however, is always boolean. When the output is on (has the value 1), the perceptron is said to be firing (the name comes from biology: when neurons send a signal in the brain, they are said to be firing).
All of the inputs (including the bias) have weights attached to the input line that modify the input value. The weight is just multiplied with the input, so if the input value was 4 and the weight was -2, the weighted input value would be -8.
The threshold is one of the key components of the perceptron. It determines, based on the inputs, whether the perceptron fires or not. Basically, the perceptron takes all of the weighted input values and adds them together. If the sum is above or equal to some value (called the threshold) then the perceptron fires.
S: http://www.aihorizon.com/essays/generalai/perceptrons.htm (last access: 28 February 2015)
N: 1. Based on word perception, from noun percept and word-forming element -tron (in compounds coined in physics, “having to do with electrons or subatomic particles,” 1939, abstracted unetymologically from electron; Greek -tron was an instrumentive suffix).
perceptrons, a type of artificial neural network investigated by Frank Rosenblatt, beginning in 1957, at the Cornell Aeronautical Laboratory at Cornell University in Ithaca, New York. Rosenblatt made major contributions to the emerging field of artificial intelligence (AI), both through experimental investigations of the properties of neural networks (using computer simulations) and through detailed mathematical analysis. Rosenblatt was a charismatic communicator, and there were soon many research groups in the United States studying perceptrons. Rosenblatt and his followers called their approach connectionism to emphasize the importance in learning of the creation and modification of connections between neurons.
2. The perceptron is a mathematical model of a biological neuron. While in actual neurons the dendrite receives electrical signals from the axons of other neurons, in the perceptron these electrical signals are represented as numerical values. At the synapses between the dendrite and axons, electrical signals are modulated in various amounts. This is also modeled in the perceptron by multiplying each input value by a value called the weight. An actual neuron fires an output signal only when the total strength of the input signals exceed a certain threshold. We model this phenomenon in a perceptron by calculating the weighted sum of the inputs to represent the total strength of the input signals, and applying a step function on the sum to determine its output. As in biological neural networks, this output is fed to other perceptrons.
3. In the field of Artificial Intelligence: A Perceptron is a system for classifying objects … that computes features of the stimulus display, then attempts to discriminate among different classes of displays by computing linear additive functions of these features.
Used as a pattern recognition device with threshold logic units, that learns from examples.
S: 1. OED – http://www.etymonline.com/index.php?allowed_in_frame=0&search=-tron&searchmode=none (last access: 28 February 2015); FCB; EncBrit – http://global.britannica.com/EBchecked/topic/752949/perceptrons (last access: 28 February 2015). 2. http://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/Neuron/index.html (last access: 28 February 2015). 3. TERMIUMPLUS.
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