Topic: synaptic parameters

Model fitting methods have been widely used in neuroscience to infer the parameters of a biophysical system from its responses to experimental stimulations. For instance, the parameters of a chemical synapse (e.g. the number of presynaptic vesicles, or its depression time constant) can be estimated from its postsynaptic responses to evoked stimuli. However, these estimates critically depend on the stimulation protocol being used. Experiments are often conducted with non-adaptive stimulation protocols that may not yield enough information about these parameters. Here, we propose using Bayesian

The post status epilepticus (SE) model is one of the most helpful animal models for epilepsy research. In many cases it closely mimics the clinical signs of temporal lobe epilepsy in humans. Although behavioral, electrophysiological and molecular alterations following SE have been extensively studied, corresponding structural changes remain less well explored. However, to optimize the treatment of epilepsy and complications of SE, it is essential to know how effectively they prevent seizure-induced neuronal damage. In the present study, we elucidate ultrastructural changes and quantitative alt