Artificial Neuron - Types of Transfer Functions

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 multilayer 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.

Below, u refers in all cases to the weighted sum of all the inputs to the neuron, i.e. for n inputs,


u = \sum_{i = 1}^n w_{i} x_{i}

where w is a vector of synaptic weights and x is a vector of inputs.

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