The transfer function as a tool to reduce morphological models into point-neuron models

Abstract

Building a simple model that precisely and functionally characterizes a neuron is a challenging and important task to select the best concise and computationally efficient model. However, this type of work has only been done for subthreshold properties of neurons. Here, we take a different perspective and suggest a method to obtain point-neuron models from morphologically-detailed models with dendrites. To do this, we focus on the functional characterization of the neuron response under in vivo conditions, and compute the transfer function of the detailed model. The parameters of this transfer function, in terms of mean voltage, voltage standard deviation and correlation time, can be used to compute the "best" point-neuron model that generates a transfer function very close to that of the morphologically-detailed model. We illustrate this approach for two very different neuronal morphologies, one from Drosophila larvae and one from mammals. In conclusion, this approach provides a tool to generate point-neuron models from detailed models, based on a functional characterization of the neuron response. Significance StatementThis study provides a new computational method to reduce morphological models into point-neuron models. To do so, we calculate the transfer function parameters, ie the voltage standard deviation, the mean voltage and the correlation time, of the morphological model and fit a point neuron-model onto this data. Here, we successfully apply this approach for two very different neuron morphologies, a drosophila neuron and a rat motoneuron.