Computational Neuroscience
Computational Neuroscience
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During my PhD, I proposed new analytical methods using binomial models to predict patterns from experimental data, which helped us better understand how neurons work. I also developed and used Monte Carlo simulations to make sense of complex data, ensuring that our experimental findings matched up with theoretical models for a complete picture.
Synaptic transmission strength varies with repeated nerve signals due to synaptic vesicle availability. Some synapses weaken, others strengthen, or show both patterns.Â
Comparing models with multiple vesicle sites, my findings show that vesicle release and replenishment affect synaptic strength. A complex two-step model explains common patterns of strengthening followed by weakening. Data from cerebellar synapses supports this model, highlighting mechanisms behind short-term changes in synaptic strength.
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