Modeling Stochastic Auditory Nerves Behavior Based on Computational Neuroscience Model Using Artificial Neural Networks
碩士 === 義守大學 === 電機工程學系碩士班 === 94 === Neural response to electrical stimulation can be modeled by Generalized Schwarz Eikhof and Frijns (GSEF) equations. They are deterministic and computational intensive. On the other hand, real neural response to electrical stimulation is stochastic. This makes GSE...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2006
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/56857822916739155478 |
| Summary: | 碩士 === 義守大學 === 電機工程學系碩士班 === 94 === Neural response to electrical stimulation can be modeled by Generalized Schwarz Eikhof and Frijns (GSEF) equations. They are deterministic and computational intensive. On the other hand, real neural response to electrical stimulation is stochastic. This makes GSEF model unattractive for realistic neural engineering application. In order to model the stochastic behavior of an electrically stimulated nerve, an artificial neural network (ANN) is used to model the GSEF with stochastic response. Once the ANN is trained, the neural response is readily available without the computation delay similar to those of the GSEF models.
|
|---|