Synapses

These components allow the user to choose the type of synapse that connects two layers.

In the toolbar there are several buttons shown above, representing two main types of synpase, these are supervised synapses and unsupervised synapses.

Note : Users should not mix, unsupervised and supervised network elements. Doing so will result in unspecified behaviour and your network will be useless. On the other hand you may first train an unsupervised network and embed it into the start of supervised i.e behind the supervised components. You can do this by exporting the unsupervised network after training and loading it into a Nested ANN component. This component must be placed before any supervised components i.e immediately after the input synapse.

Supervised Synapses

A Full Synapse will connect every output connection of one layer to this input of every neuron in another layer.

A Direct Synapse will connect each output connection of one layer to exactly one neuron in the other layer. The number of outputs of the first layer must match the number of neurons in the second layer, or an exception will be generated.

A Delayed Synapse behaves as a full Synapse where each connection is implemented with a FIRFilter object. In this connection is implemented the temporal backpropagation algorithm by Eric A. Wan, as in 'Time Series Prediction by Using a Connectionist Network with Internal Delay Lines ' in Time Series Prediction. Forecasting the Future and Understanding the Past, by A.Weigend and N.Gershenfeld. Addison-Wesley, 1994.

Unsupervised Synapses

A Sanger Synapse to build unsupervised neural networks that apply the PCA (Principal Component Analysis) algorithm. Using this synapse along with the Nested Neural Network component is very easy to build modular neural networks where the first NN acts as a pre-processing element that reduces the number of the input columns and its noise.

A Kohonen synapse is used along with a SOM type layer (e.g Winner Takes All or Gaussian Layer) to characterise data. For example this synapse could be used to determine whether an image is showing a character 'A' or 'B' as explained in the Joone Image Recognition tutorial. It is important that this component is only connected to a SOM layer, failure to do so will result in unspecified behaviour. Effectively this synapse implements the weights of a SOM network and the SOM layers implement the SOM learning strategy.

To use these components, the firstly selects the tool button corresponding to the synapse required, then drag a line from one layer to another in the drawing area. The newly inserted synapse will be shown with an arrow containing a small box at its centre, as in the following figure:

synapses sample

The box contains a label indicating the type and state of the synapse. The available types are shown the following table:

F = Full Synapse
D = Direct Synapse
-1 = Delayed Synapse
S = Sanger PCA Synapse
K = Kohonen Synapse

If the little box is greyed, then the synapse is disabled, indicating that this branch of the net is interrupted. To disable a synapse, right-click it and set the 'Enabled' property in the property panel to false. This feature is very useful in the designing of a neural network to try several architectures on the fly.