Abstract
Multi-compartmental models of neurons provide insight into the complex, integrative properties of dendrites. Because it is not feasible to experimentally determine the exact density and kinetics of each channel type in every neuronal compartment, an essential goal in developing models is to help characterize these properties. To address biological variability inherent in a given neuronal type, there has been a shift away from using hand-tuned models towards using ensembles or populations of models. In collectively capturing a neuron's output, ensemble modeling approaches uncover important conductance balances that control neuronal dynamics. However, conductances are never entirely known for a given neuron class in terms of its types, densities, kinetics and distributions. Thus, any multi-compartment model will always be incomplete. In this work, our main goal is to use ensemble modeling as an investigative tool of a neuron's biophysical balances, where the cycling between experiment and model is a design criterion from the start. We consider oriens-lacunosum/moleculare (O-LM) interneurons, a prominent interneuron subtype that plays an essential gating role of information flow in hippocampus. O-LM cells express the hyperpolarization-activated current (I h). Although dendritic I h could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of I h on O-LM dendrites is not known. Using a high-performance computing cluster, we generated a database of models that included those with or without dendritic I h. A range of conductance values for nine different conductance types were used, and different morphologies explored. Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties. Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic I h. These findings inform future experiments that differentiate between somatic and dendritic I h, thereby continuing a cycle between model and experiment.
Highlights
Neurons possess diverse, elaborate morphologies that allow the propagation of electrical signals through fine, intricate neuronal structures [1]
We considered how one might assess whether dendritic Ih is present in O-LM cells, noting that we already know that Ih is present in O-LM cells [19], so it is not a matter of applying pharmacological blockers
We developed a cyclical ensemble modeling approach (Fig. 1) for investigating the interaction of voltage-gated conductance densities and distributions that give rise to intrinsic, cellular output, and applied it to O-LM cells
Summary
Elaborate morphologies that allow the propagation of electrical signals through fine, intricate neuronal structures [1]. Due to the pioneering work of Rall [2], many multicompartment mathematical models of different neurons have been developed. Having multi-compartment models in hand allows us to explore aspects not possible or feasible to do experimentally, and to provide mechanistic insights into experimental observations and paradoxical results [3,4]. Our developed models are limited by the lack of knowledge of the biological details. For any given neuron type, there are inevitable uncertainties regarding the properties of each electrical compartment of the modeled neuron that cannot be inferred from experimental observations alone. The use of multi-compartment models allows us to simulate different possibilities in terms of ion channel types and properties in order to test plausible mechanisms of neuronal function and generate predictions that can be experimentally examined
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