McCulloch-Pitts neuron - ορισμός. Τι είναι το McCulloch-Pitts neuron
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Τι (ποιος) είναι McCulloch-Pitts neuron - ορισμός

MATHEMATICAL FUNCTION CONCEIVED AS A CRUDE MODEL
McCuloch-Pitts neuron; McCulloch-Pitts neuron; Threshold Logic Unit; Node (neural networks); Artificial neurons; Nv neurons; McCulloch–Pitts neuron; Binary neuron; McCulloch Pitts Neuron; MCP neuron; Linear neuron; Linear threshold unit; User:Justprajwal/sandbox; Wikipedia talk:Articles for creation/Linear Threshold Unit; Activation (neural network); Nv neuron
  • Neuron and myelinated axon, with signal flow from inputs at dendrites to outputs at axon terminals

McCulloch-Pitts neuron         
<artificial intelligence> The basic building block of artificial neural networks. It receives one or more inputs and produces one or more identical outputs, each of which is a simple non-linear function of the sum of the inputs to the neuron. The non-linear function is typically a threshhold or step function which is usually smoothed (i.e. a sigmoid) to facilitate learning. (1997-10-11)
Artificial neuron         
An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in an artificial neural network.
USS McCulloch (1897)         
  • United States Coast Guard ensign (1915–1953)
  • United States Revenue Cutter Service ensign (1868–1915)
  • ''McCulloch'' sinking on 13 June 1917.
AMERICAN REVENUE SERVICE CUTTER
USRC McCulloch (1897); USS McCulloch (1897)
USS McCulloch, previously USRC McCulloch and USCGC McCulloch, was a ship that served as a United States Revenue Cutter Service cutter from 1897 to 1915, as a United States Coast Guard Cutter from 1915 to 1917, and as a United States Navy patrol vessel in 1917. She saw combat during the Spanish–American War during the Battle of Manila Bay and patrolled off the

Βικιπαίδεια

Artificial neuron

An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in an artificial neural network. The artificial neuron receives one or more inputs (representing excitatory postsynaptic potentials and inhibitory postsynaptic potentials at neural dendrites) and sums them to produce an output (or activation, representing a neuron's action potential which is transmitted along its axon). Usually each input is separately weighted, and the sum is passed through a non-linear function known as an activation function or transfer function. The transfer functions usually have a sigmoid shape, but they may also take the form of other non-linear functions, piecewise linear functions, or step functions. They are also often monotonically increasing, continuous, differentiable and bounded. Non-monotonic, unbounded and oscillating activation functions with multiple zeros that outperform sigmoidal and ReLU like activation functions on many tasks have also been recently explored. The thresholding function has inspired building logic gates referred to as threshold logic; applicable to building logic circuits resembling brain processing. For example, new devices such as memristors have been extensively used to develop such logic in recent times.

The artificial neuron transfer function should not be confused with a linear system's transfer function.

Artificial neurons can also refer to artificial cells in neuromorphic engineering (see below) that are similar to natural physical neurons.