Competitive Group
In relation to Competitive Network, a competitive group is an unit of a collection of neurons within a network in which the neurons compete with each other to represent clusters of inputs. This page covers common properties found within a competitive group.
Creation
Update Method: Update to current method to Rumelhart-Zipser or Alvarez-Squire.
Epsilon: This field sets the standard learning rate to determine how quickly synapses change.
Winner Value: Insert value for the winning neuron in this field.
Loser Value: Insert the value for all losing neurons in this field.
Use Leaky Learning: By checking this field, all weights learn on each time step, rather than just the winning weight (WTA).
Leaky Epsilon: The learning rate for losing neurons, when leaky learning is enabled.
Normalize Inputs: By checking this field, the inputs would be normalized prior to being used in setting weights.
Synapse Decay Percent: This field sets how much the synapse will decay.
Layout
Neuon Layout: This field lets the user choose perferred neuron layout.
Layout Style: This field lets the user choose perferred layout style of the neurons. See neuron layout for details.
Spacing: This field sets the distance between the neurons.
Edit
Summary
Parent: The parent group type, if any.
Incoming
Outgoing
Neurons
Basic Data:Activation level is set in this field for the group of neurons. Additionally, labelling of the neurons are set in this field.
Update Rule: Neuron type can be updated in this field.
Layout
Neuron Layout: Select preferred neuron layout in this field.
Horizontal Spacing: The distance between the neurons from left to right
Vertical Spacing: The distance between the neurons up and down.
Manual Columns: Check this field if user wishes to manually set number of columns for the group.
Number of Columns: The number of preferred columns.
Input Data
User can input data here manually or by uploading .csv file.
Right Click Menu
Edit/Train Competitive Network: Opens a dialog for editing and training the network.
Rename: Rename the network in this field.
Remove Network: Deletes the network and it's constitutes.
Add Current Pattern to Input Data: By clicking on this selection, it takes the current pattern and input it as the input data.
Train on Current Pattern: Train the network with the current pattern input data.
Randomize Weights: Selecting this option randomizes the synaptic weights.