Studying the biophysical properties of neurons, such as the different ion channel distributions and kinetics, can be done by fitting in silico neuronal models to in vitro neuronal recordings. In this process, a detailed neuron is modeled to a set of electrical circuits that describe its biophysical properties. The goal is to constrain the in-silico model’s parameters so it will behave like the in vitro neurons recorded during the experiment. This process is done using an optimization algorithm that relies on training the model across thousands of permutations.
We have developed a pipeline to fit the models to experimental data and we are now working on improving these models to develop highly detailed biophysical simulations for recorded neurons.