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- W2906094425 abstract "Little research has been done regarding the use ofmodeling techniques to estimate the connectivity of a nervoussystem by analyzing data recorded from its neurons. In this thesis,three main methods were implemented to solve this task: Bayesianinference, artificial neural networks, and ODE reverse engineering.The goal is to apply these methods to data recorded from C. elegansneurons and estimate the connections between these. The top twoperforming methods on simulated data were feed-forward neuralnetworks and ODE reverse engineering. The worst performing methodswere recurrent neural networks and Bayesian inference. Out of themethods tried, feed-forward neural network was the most robust tochanges in parameters of the simulation network and noise.Furthermore, this technique is easily generalizable since it didnot rely on any particular feature of the simulation network toachieve its good performance. Nevertheless, all methods are easilyreproducible for any further research." @default.
- W2906094425 created "2019-01-01" @default.
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- W2906094425 date "2017-01-01" @default.
- W2906094425 modified "2023-09-26" @default.
- W2906094425 title "Modeling neural connectivity of caenorhabditis elegans" @default.
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