| 000 | 01886cam a2200337 a 4500 | ||
|---|---|---|---|
| 003 | EG-GiCUC | ||
| 005 | 20250223031306.0 | ||
| 008 | 150908s2014 ua de f m 000 0 eng d | ||
| 040 |
_aEG-GiCUC _beng _cEG-GiCUC |
||
| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aM.Sc | ||
| 099 | _aCai01.13.10.M.Sc.2014.Al.S | ||
| 100 | 0 | _aAlmoataz-Bellah Mohamed Alsayed Hegab | |
| 245 | 1 | 0 |
_aSimplified neuron model with memristive ionic channels / _cAlmoataz-Bellah Mohamed Alsayed Hegab ; Supervised Noha Mohamed Salem , Ahmed Gomaa Radwan |
| 246 | 1 | 5 | _aنموذج مبسط للخلية العصبية باستخدام مقاوم ذى ذاكرة لتمثيل المسارات الأيونية |
| 260 |
_aCairo : _bAlmoataz-Bellah Mohamed Alsayed Hegab , _c2014 |
||
| 300 |
_a71 P. : _bcharts , plans ; _c30cm |
||
| 502 | _aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Mathematics and Physics | ||
| 520 | _aFirst of all, we have introduced a simplified neuron model, however it produces an action potential that follows the original Hodgkin and Huxley model. The genetic optimization algorithm has been used in order to enhance the modulating parameters of the proposed model. The ion channel equations in the simplified neuron model have been generalized to represent a simplified memristor model that could approximate different experimental results of memristive devices. This approach could be considered as a step forward in the creation of a nano-scale memristor based neural network | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aHodgkin and Huxley Equations | |
| 653 | 4 | _aMemristors | |
| 653 | 4 | _aNeuron Model | |
| 700 | 0 |
_aAhmed Gomaa Radwan , _eSupervisor |
|
| 700 | 0 |
_aNoha Mohamed Salem , _eSupervisor |
|
| 856 | _uhttp://172.23.153.220/th.pdf | ||
| 905 |
_aEnas _eCataloger |
||
| 905 |
_aNazla _eRevisor |
||
| 942 |
_2ddc _cTH |
||
| 999 |
_c52281 _d52281 |
||