Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2014.317.324AlmasriAbdullah H.SahranShahnorbanun42014144Assigning threshold value plays an important role in the temporal
coding Spiking Neural Network (SNN) as it determines when the neuron should
fire, the time window parameter plays a significant role in the SNN performance.
This study does two things: First it proposes a mathematical method to find
out the threshold boundary in the temporal coding SNN models and second it outlines
the input time window boundary which leads to specify the spike time boundary.
The latter was used at the former. The threshold boundary method was applied
to two learning algorithms i.e., Spiking-Learning Vector Quantization (S_LVQ)
and Self-Organizing Weight Adaption for SNN (SOWA_SNN), for both classification
and clustering pattern recognition applications, respectively. This method finds
the threshold boundary mathematically in both learning models above and observes
that the minimum and maximum value of the threshold does not depend on the time
input window, time coding or delay parameters in SNN. With regard to the input
time window, it finds that specification beyond the parameter boundary affects
the computational network cost and performance; also it finds that the delay
and the time coding parameters play a significant role in assigning the time
window boundary.]]>Bohte, S.M., J.N. Kok and H.L. Poutre,2002Bohte, S.M., H. la Poutre and J.N. Kok,2002Hopfield, J.J.,1995Xin, J. and M.J. Embrechts,2001Maass, W.,1997Natschlager, T. and B. Ruf,1998Pham, D.T., M.S. Packianather and E.Y.A. Charles,2007Pham, D.T., M.S. Packianather and E.Y.A. Charles,2008Pham, D.T. and S. Sahran,2006Ruf, B. and M. Schmitt,1997Shahnorbanun, S., S.A.S.N. Huda, A. Haslina, O. Nazlia and H. Rosilah,2010Sporea, I. and A. Gruning,2012Maass, W. and C.M. Bishop,2001