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BASIC STRUCTURE For a given artificial neuron, let there be ''m'' inputs with signals ''x''1 through ''x''''m'' and weights ''w''1 through ''w''''m''. The output of neuron ''k'' is: : Where (Phi) is the transfer function. The output propagates to the next layer (through a weighted synapse) or finally exits the system as part or all of the output. HISTORY The original artificial neuron is the Threshold Logic Unit first proposed by Warren McCulloch and Walter Pitts in 1943 . As a transfer function, it employs a ''threshold'' or Step Function taking on the values 1 or 0 only. ''See article on Perceptron for more details'' TYPES OF TRANSFER FUNCTIONS The transfer function of a neuron is chosen to have a number of properties which either enhance or simplify the network containing the neuron. Crucially, for instance, any Multi-layer Perceptron using a ''linear'' transfer function has an equivalent single-layer network; a non-linear function is therefore necessary to gain the advantages of a multi-layer network. Step function The output ''y'' of this transfer function is binary, depending on whether the input meets a specified threshold, ''θ''. The "signal" is sent, i.e. the output is set to one, if the activation meets the threshold. : See: Step Function Sigmoid A fairly simple non-linear function, the Sigmoid also has an easily calculated derivative, which is used when calculating the weight updates in the network. It thus makes the network more easily manipulable mathematically, and was attractive to early computer scientists who needed to minimise the computational load of their simulations. See: Sigmoid Function SEE ALSO Connectionism BIBLIOGRAPHY
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