Temat: spiking neural networks - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Novel GPU-Enabled Simulator for Large Scale Spiking Neural Networks
Autorzy:: Szynkiewicz, P.
Powiązania:: https://bibliotekanauki.pl/articles/307680.pdf
Data publikacji:: 2016
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: GPU computing
OpenCL programming technology
parallel simulation
spiking neural networks
Opis:: The understanding of the structural and dynamic complexity of neural networks is greatly facilitated by computer simulations. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper a framework for modeling and parallel simulation of biological-inspired large scale spiking neural networks on high-performance graphics processors is described. This tool is implemented in the OpenCL programming technology. It enables simulation study with three models: Integrate-andfire, Hodgkin-Huxley and Izhikevich neuron model. The results of extensive simulations are provided to illustrate the operation and performance of the presented software framework. The particular attention is focused on the computational speed-up factor.
Źródło:: Journal of Telecommunications and Information Technology; 2016, 2; 34-42
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Analysis of the resume learning process for spiking neural networks
Autorzy:: Ponulak, F.
Powiązania:: https://bibliotekanauki.pl/articles/907949.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: uczenie nadzorowane
sieć neuronowa
analiza parametryczna
supervised learning
spiking neural networks
parametric analysis
learning window
Opis:: In this paper we perform an analysis of the learning process with the ReSuMe method and spiking neural networks (Ponulak, 2005; Ponulak, 2006b). We investigate how the particular parameters of the learning algorithm affect the process of learning. We consider the issue of speeding up the adaptation process, while maintaining the stability of the optimal solution. This is an important issue in many real-life tasks where the neural networks are applied and where the fast learning convergence is highly desirable.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2008, 18, 2; 117-127
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Comparison of supervised learning methods for spike time coding in spiking neural networks
Autorzy:: Kasiński, A.
Ponulak, F.
Powiązania:: https://bibliotekanauki.pl/articles/908459.pdf
Data publikacji:: 2006
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: uczenie nadzorowane
sieć neuronowa
kodowanie czasowe
sekwencja czasowa
supervised learning
spiking neural networks
time coding
temporal sequences of spikes
Opis:: In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods? In order to answer this question, we discuss various approaches to the learning task considered. We shortly describe the particular learning algorithms and report the results of experiments. Finally, we discuss the properties, assumptions and limitations of each method. We complete this review with a comprehensive list of pointers to the literature.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2006, 16, 1; 101-113
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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