Electrophysiology prediction of single neurons based on their morphology

Abstract

Electrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon). In silico biophysical models are therefore created for simulating hundreds of experiments that would be impractical to recreate in vitro. The optimal way to create these models is based on the knowledge of the morphological and electrical features for each neuron. Since large-scale data acquisition is often unfeasible for electrical data, previous expert knowledge can be used but it must have an acceptable degree of similarity with the type of neurons that we are trying to model. Here, we present a data-driven machine learning approach to predict the electrophysiological features of single neurons in case of only having their morphology available. To solve this multi-output regression problem, we use an artificial neural network that has the particularity of providing a probability distribution for every output feature, to incorporate uncertainty. Input data to train the model is obtained from from the Allen Cell Types database. The electrical properties can depend on the morphology, whose acquisition technology is highly automated and scalable so there exist large data sets of them. We also provide integrations with the BluePyOpt library to create a biophysical model using the original morphology and the predicted electrical features. Finally, we connect the resulting biophysical model with the Geppetto UI software to run all the simulations in a sophisticated user interface.