2006 IEEE Workshop on Nonlinear Circuit Networks (NCN'06)
Tokushima University, Tokushima, Japan, Dec. 9, 2006

SOM Considering False Neighboring Neuron
for Effective Feature Extraction
Haruna Matsushita and Yoshifumi Nishio
Department of Electrical and Electronic Engineering,
Tokushima University
Email: haruna@ee.tokushima-u.ac.jp, nishio@ee.tokushima-u.ac.jp

Abstract— In this study, we propose a new Self-Organizing
Map (SOM) algorithm which considers the False Neighboring
Neuron (called FNN-SOM). The FNN-SOM self-organizes withconsidering the real neighboring relation. We confirm that we canobtain the more effective map reflecting the distribution state of
input data than the conventional SOM.

I. I NTRODUCTION
In real world, it is not always true that the next-door house
is close to my house, in other words, “neighbors” are notalways “true neighbors”. In addition, the synaptic strengthis not constant in the brain. There is scarcely any researchchanging the synaptic strength.
In this study, we propose a new Self-Organizing Map(SOM) algorithm which considers the False Neighboring Neuron (called FNN-SOM). The FNN-SOM self-organizes withconsidering the real neighboring relation. The neuron, whichis the most distant from the input data in a set of direct topological neighbors of winner, is defined as “False NeighboringNeuron (FNN)”. Furthermore, all of the connection between
each neuron of FNN-SOM has the connection reference vector.
The connection reference vectors of FNN are increased, andthe connection reference vectors act as burden of the distance
between each map node when the weight vectors of neurons
are updated.
We confirm that we can obtain the more effective map
reflecting the distribution state of input data than the conventional SOM [1].

winner c and the each neuron. In other words, the neuron,
which is closer to the winner and whose reference vector issmaller, is more significantly updated.
III. L EARNING S IMULATION
We carry out learning simulation for the “C”-shaped 2dimensional data, shown in Fig. 1(a). The total number if data
is 700. All the input data are sorted by random.
The leaning conditions are as follows. Both the conventional
SOM and FNN-SOM has 289 neurons (17 × 17). We repeat
the learning 15 times for all input data.
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II. SOM WITH FALSE N EIGHBORING N EURON(FNN-SOM)

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00We explain a proposed new SOM algorithm, FNN-SOM.
All of the connection between each neuron of FNN-SOM has
the connection reference vector.
An input vector is inputted to all the neurons at the sametime in parallel. Distances between the input vector and all
the weight vectors are calculated, and the winner c is definedas the neuron which is closest to the input vector. A False
Neighboring Neuron l (called FNN) is found. FNN is the most
distant from the input vector in N c1 , where N c1 is the setof direct topological neighbors of c. The connection reference
vector between c and l is increased. Conversely, the connectionreference vectors between c and its neighbors N c1 without l
are set to zero. The weight vectors of the neurons are updatedby considering the connection reference vector between the0.2

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Fig. 1.
Learning Simulation. (a) Input data. (b) Simulation result ofconventional SOM. (c) Simulation result of FNN-SOM.

The results of SOM and FNN-SOM are shown in Figs. 1(b)and (c). We can see that there are some inactive neurons in
the result of SOM, however, there are no inactive neurons in
the result of FNN-SOM. This is because the neurons of FNNSOM are not affected by FNN, so, the neurons can learn moredistant for the distant input data. Therefore, FNN-SOM canobtain the more effective map reflecting the distribution stateof input data, than the conventional SOM.
R EFERENCES[1] T. Kohonen, Self-Organizing Maps, Berlin, Springer, vol. 30, 1995.

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