Year: 2026
Type: article
Abstract: BACKGROUND: 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. NEW METHOD: Here, we take a different perspective and propose a method to obtain point-neuron models from morphologically-detailed models with dendrites, preserving their transfer-function properties and firing rate statistics under in vivo-like conditions. RESULTS: 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-matching 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. COMPARISON WITH EXISTING METHODS: This approach provides a tool to generate point-neuron models from detailed models, based on a functional characterization of the neuron response, while previous methods focused on subthreshold (passive) characterization. CONCLUSIONS: This study provides a new computational method to reduce morphological models into point-neuron models that reproduce their transfer-function properties and firing rate statistics under in vivo-like conditions.
Source: Journal of Neuroscience Methods
Authors: Mikal Daou, Tihana Jovanic, Alain Destexhe
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,900
Award: G1714039997
OpenAlex ID: W7204184847