Abstract
This manuscript is focused on some applications of method Spikeprop of Spiking Neural Networks (SNN) using an especific hardware for parallel programming in order to measure the eficience. So, we are interested on pattern recognition and clustering, that are the main problems to solve for Artificial Neural Networks (ANN). As a result, we are going to know the considerations,its limitations and advantages, that we have to take into account for applying SNN. The main advantage is that the quantity of applications can be expanded for real applications linear or non linear, with more than one attribute, and big volume of datas. In contrast, other methods spend a lot of memory to process the information, which computational complexity is propotional to the volume and quantity of attributes of datas, also is more difficult to program the algorithm for multiclass database. However, the main limitation of SNN is the convergence, that tends forward a Local minimum Value. This implies a high dependency on the configuration and proposed architecture. On the other hand, we programmed the algorithm of SNN in a GPU model NVIDIA GeForce 9400 M. In this GPU we had to reduce parallelism in order to increase quantity of layers and neurons in the same hardware in spite of contains 60000 threads, they were not enought. On the otrher hand, the divergence is reduced when the database is bigger for database multiclass.
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Paz, I.T., Hernández Gress, N., González Mendoza, M. (2013). Pattern Recognition with Spiking Neural Networks. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_25
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DOI: https://doi.org/10.1007/978-3-642-45111-9_25
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