| Title: | Gradient method of learning for stochastic kinetic model of neuron |
| Authors: | Świetlicka, Aleksandra Gugała, Karol Karón, Igor Kolanowski, Krzysztof Majchrzycki, Mateusz Rybarczyk, Andrzej |
| Citation: | ISTET 2013: International Symposiumon Theoretical Electrical Engineering: 24th – 26th June 2013, Pilsen, Czech Republic, p. III-17-III-18. |
| Issue Date: | 2013 |
| Publisher: | University of West Bohemia |
| Document type: | konferenční příspěvek conferenceObject |
| URI: | http://hdl.handle.net/11025/11489 |
| ISBN: | 978-80-261-0246-5 |
| Keywords: | stochastický kinetický model neuronu;gradientní metoda učení;Hodgin-Huxleyho model |
| Keywords in different language: | stochastic kinetic model of neuron;gradient method of learning;Hodgkin-Huxley model |
| Abstract: | In this paper we are focusing on the kinetic extension [4] of classic model of Hodgkin and Huxley [2]. We are showing the descent gradient method used in the learning process of neuron, which is described with stochastic kinetic model. In comparison with [1] we use only 3 weights instead of 9: gNa; gK and gL: We show that this model behaves equally accurate as the model of Hodgkin and Huxley with slighter system description. |
| Rights: | © University of West Bohemia |
| Appears in Collections: | ISTET 2013 ISTET 2013 ISTET 2013 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Swietlicka.pdf | Plný text | 160,23 kB | Adobe PDF | View/Open |
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