How is the winning neuron selected by the NEWSOM function within Neural Network Toolbox?

I would like to know how the winning neuron is selected by the NEWSOM function within Neural Network Toolbox.

ANSWER

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The NEWSOM function is used to obtain a self-organizing network.

The syntax for this function is as follows:

net = newsom(PR,[d1,d2,...],tfcn,dfcn,olr,osteps,tlr,tns)
Description
 Competitive layers are used to solve classification problems.
 NET = NEWSOM(PR,[D1,D2,...],TFCN,DFCN,OLR,OSTEPS,TLR,TNS) takes,
 PR     - Rx2 matrix of min and max values for R input elements.
 Di     - Size of ith layer dimension, defaults = [5 8].
 TFCN   - Topology function, default = 'hextop'.
 DFCN   - Distance function, default = 'linkdist'.
 OLR    - Ordering phase learning rate, default = 0.9.
 OSTEPS - Ordering phase steps, default = 1000.
 TLR    - Tuning phase learning rate, default = 0.02;
 TND    - Tuning phase neighborhood distance, default = 1.
    and returns a new self-organizing map.

How the winning neuron is selected:

When an input topology is presented to a SOM network,

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How is the winning neuron selected by the NEWSOM function within Neural Network Toolbox?

How is the winning neuron selected by the NEWSOM function within Neural Network Toolbox?