Controlling morpho-electrophysiological variability of neurons with detailed biophysical models
Variability is a universal feature among biological units such as neuronal cells as they enable a robust encoding of a high volume of information in neuronal circuits and prevent hypersynchronizations such as epileptic seizures. While most computational studies on electrophysiological variability in neuronal circuits were done with simplified neuron models, we focus on the variability of detailed biophysical models of neurons. Using measures of experimental variability, we leverage a Markov chain Monte Carlo method to generate populations of electrical models that can reproduce the variability