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## Uses of Class
org.joone.engine.SimpleLayer

Packages that use SimpleLayer
org.joone.engine |

Uses of SimpleLayer in org.joone.engine

Subclasses of SimpleLayer in org.joone.engine
`class` | `BiasedLinearLayer` This layer consists of linear neurons, i.e. neurons that sum up their inputs (actually this is done by the (full) synapse in Joone) along with their biases.
`class` | `ContextLayer` The context layer is similar to the linear layer except that it has an auto-recurrent connection between its output and input.
`class` | `GaussianLayer` This layer implements the Gaussian Neighborhood SOM strategy.
`class` | `GaussLayer` The output of a Gauss(ian) layer neuron is the sum of the weighted input values, applied to a gaussian curve (`exp(- x * x)`).
`class` | `LinearLayer` The output of a linear layer neuron is the sum of the weighted input values, scaled by the beta parameter.
`class` | `LogarithmicLayer` This layer implements a logarithmic transfer function.
`class` | `SigmoidLayer` The output of a sigmoid layer neuron is the sum of the weighted input values, applied to a sigmoid function.
`class` | `SineLayer` The output of a sine layer neuron is the sum of the weighted input values, applied to a sine (`sin(x)`).
`class` | `SoftmaxLayer` The outputs of the Softmax layer must be interpreted as probabilities.
`class` | `TanhLayer` Layer that applies the tangent hyperbolic transfer function to its input patterns
`class` | `WTALayer` This layer implements the Winner Takes All SOM strategy.

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