In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step function as the activation function.
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SNNS is written around a simulation kernel to which user written activation functions, learning procedures and output functions can be added.
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In computational networks, the "'activation function "'of a node defines the output of that node given an input or set of inputs.
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The final model, then, that is used in multilayer perceptrons is a sigmoidal activation function in the form of a hyperbolic tangent.
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A wide variety of sigmoid functions have been used as the activation function of artificial neurons, including the logistic and hyperbolic tangent functions.
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Compositional pattern-producing networks ( CPPNs ) are a variation of ANNs which differ in their set of activation functions and how they are applied.
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The possibility of differentiating the activation function allows the direct use of the gradient descent and other optimization algorithms for the adjustment of the weights.
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A coactivator protein interacting with the activation function 1 ( AF-1 ) transactivation domain of the androgen receptor may have been deficient in this patient.
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In 1989, the first proof was published by George Cybenko for sigmoid activation functions and was generalised to feed-forward multi-layer architectures in 1991 by Kurt Hornik.
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Our output activation function is also allowed to be nonlinear and nonstatic, but it must be continuously differentiable in both and and have derivatives bounded in time.
