Self-Organized Spiking Neural Network Model for DataClustering
J.Bidin and M.K.M.Amin

Graduate School of Electronic System Engineering, Malaysia-Japan International Institute of Technology (MJIIT),
University Technology Malaysia International Campus, Kuala Lumpur, Malaysia
1Introduction

In recent modern era of neural networks technology, amodel called Spiking Neural Network (SNN) was born. This
SNN was classified by Maass [1] as the third generation ofneural networks. It is a new kind of neural network which isinspired and motivated by the biological neurons ways ofcommunication. The biological neurons communicate with eachother through the media of action potentials, often called pulses
or spikes. This is a well-known aspect of real neurons, whichtransmit information by voltage pulses of the membranepotential.
SNN has been much considered in attempt to achieve amore biologically inspired artificial neural network. The
objective of the SNN as the name implies, tries to overcome theover simplification of the ANN system and emulate the pulse
system to come out with a more biologically realistic neuralsystem.
This paper presents a preliminary investigation anddevelopment of the unsupervised learning processes in SNN.
This learning mechanism is extended to one of the mostsuccessful paradigm of unsupervised learning: the Kohonen
Self-Organizing Maps [3]. Hence, a Self-Organized SNN basedon Spike Respond Model (SRM) is constructed. Spiking
neurons with delays to encode the information is suggested.
Thus, each output node will produce a different timing whichenables competitive learning. The model is designed andprogrammed in MATLAB environment. Further simulation
analysis was performed to observe the network self-organizing
learning and its topology preservation behavior. The model isfurther assessed with real-world dataset for data clustering
simulation.

2Self-Organized SNN Architecture

There exist various kinds of spiking neuron models. The
neuron model presented here is based on the simplified model ofthe Spike Response Model introduced by Gerstner [2]. Thismodel is further structured to form a self-organized SNN
architecture. The network architecture is illustrated in Fig. 1.
The diagram illustrates the basic architecture of self-organized
SNN. The presynaptic neurons (the inputs) are each of themconnected to all the postsynaptic neurons (the output layer). In
this diagram the inputs are n dimensional elements and theoutput has 5x5 layer structure. Every time a set of n dimensional
element input is presented to the network, a spike potential withdifferent delays travel across each connection. These neurons
will compete with each other and through some distancemeasurement the winner is chosen. Through this process, theneurons are expected to organize themselves in the topology
preserving grid.
The Self-Organized SNN architecture can be categorized
into three main processes i.e. initialization, delay adjustment andself-organization.

Fig.1. A basic architecture of 5x5 self-organized SNN
2.1

Initialization
In this stage, the presynaptic neurons (as illustrated in Fig.1)
are pre-process before the learning phase. Initial random valuesof inputs are set and transform into temporal inputs. Assumption
is made that each neuron is firing only once i.e. one spike perneuron, each neuron of the input layers hold the value of each
inputs; dependent on the dimension of the inputs.
2.2

Delay adjustment
The connection strength of the input-output strength can bedetermined by delays. The delay is given by the difference
between the presynaptic neuron firing time and the time thepostsynaptic potential starts rising.
2.3

Self-Organization
The Self-Organized SNN organizes its neurons into an input
network layer and an output network layer. Each neuron in theinput layer is connected to each neuron in the output layer. The
two-dimensional output layers act as a map where each neuron
is positioned. For this winning measurement we use the shortest
Euclidean distance temporal inputs − delays between the input

and the competing neurons.

33.1

Simulation Results

The Self-Organized SNN
Fig. 2 displays a simulation result of the above mentioned
algorithm in section 2. In this case the inputs are twodimensional which is sequentially feed and applied to thenetwork. Each input is assigned with an arbitrarily small valueof delay. The output activation is calculated at each run bymeasuring the difference of the inputs and the delays value.

Through this process, the node that fired the quickest spike ischosen as the winner.
2−D Input Distribution
10.80.60.6

y axis10.40.2
00.4
0.2

00.5

01References
0110.80.80.60.6

y axisy axisNr iterations: 50

0.4
0.20This research was carried out with support from theElectronic
SystemEngineering Department
atMalaysia-Japan International Institute of Technology,
UniversityTechnology
Malaysia,InternationalCampus, Kuala Lumpur, Malaysia.

Nr iterations: 20

0.8

0.5
x axisNr iterations: 100

10.5
x axis

10[1]

[2]

0.4

[3]

0.20

Acknowledgements

00.5x axis

1[4]Fig. 2. Self-Organized SNN Simulation Result[5]3.2Data Clustering
The proposed Self-Organized SNN was further assessedwith real-world dataset for data clustering. The objective was toanalyze the effectiveness of the architecture on data analysis
exploration. The Glass Identification dataset from UCI
Repository [7] was used for the experiment.
141210position(2,i)

1786GLASS IDENTIFICATION−Training Results
22212122 122 2211 1112 32
1251212 31 1225111212 12223213 11311213231 131313131117161716462 16 16623221112112121211112 13 11 11 21113 211315 152160

7 27 27 17 27
1577 7 75716 16 15 2 15 1717 17 2 1757
7 507105
position(1,i)
7525 15

Fig. 3. Example of Glass data Training results with 1000
epochs4Conclusion

The experimental results showed that Spiking Neural
Networks is able to be implemented in self-organized learning.
Few publications have been reported relating to theimplementation of Spiking Neural Network in self-organization
networks [4, 5 and 6]. However, the research on Self-Organizing
Spiking Neural Network is still an open discussion. Researchersreported different models with different approaches.
The Self-Organized SNN which was presented in this paper,
contribute another perspective of the Spiking Neural Networks
unsupervised learning. The learning computes the delay of theinput-output neurons for its competitive process. This method isan advantage because a straightforward Euclidian distancemeasurement can be used.
The experimental results had also shown the application ofself-organized SNN on data clustering. The network was trainedand tested on Glass data sets. The training and testing simulation
results show that the network is capable to perform to someextent the desired results.

[6]

[7]

W. Maass, Networks of Spiking Neurons: The Third Generation of
Neural Network Models, Neural Networks, Vol. 10, No. 9, pages
1659-1671, 1997
W.Gerstner and W.Kistler, Spiking Neuron Models, Cambridge
Univ. Press, U.K, pages 7-8, 2002
T.Kohonen, Self-Organising Maps, Springer-Verlag, Germany,
2000
C.Panchev and S. Wermter,Hebbian Spike-Timing Dependent
Self-Organization in Pulsed Neural Networks. Proceedings ofWorld Congress on Neuroinformatics, 2001.
B.Ruf and M.Schmitt, Self-organization of spiking neurons using
action potential timing, IEEE Transactions on Neural Networks,
vol. 9(3), pages 575-578, 1998
D.M. Sala and K.J. Cios, Self-organization in networks of spiking
neurons, Australian Journal of Intelligent Information Processing
Systems, 5(3), pages 161-170, 1998
UCI
repositoryofmachinelearning
databases.
http://archive.ics.uci.edu/ml/, University of California, Irvine,
Dept. of Information and Computer Science, 1998