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Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells.

Sekulić V, Lawrence JJ, Skinner FK - PLoS ONE (2014)

Bottom Line: 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 Ih.These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

View Article: PubMed Central - PubMed

Affiliation: Toronto Western Research Institute, University Health Network, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada.

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 (Ih). Although dendritic Ih could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of Ih 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 Ih. 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 Ih. These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

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The cyclical ensemble modeling approach.Schematic highlighting the methodological links between stages in the ensemble modeling approach.
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pone-0106567-g001: The cyclical ensemble modeling approach.Schematic highlighting the methodological links between stages in the ensemble modeling approach.

Mentions: In this paper, we apply ensemble modeling to hippocampal O-LM cells in order to consider dendritic Ih. In doing this, we propose an experiment-modeling cycling approach (Fig. 1). The benefit of ensemble modeling has been demonstrated [5]–[8]. Our intent with the cycling approach here is to take advantage of it in the context of hippocampal interneurons. Importantly, we focus on multi-compartment models to allow consideration of non-somatic properties since, experimentally, this is where the most challenging aspects lie, and where functionally relevant aspects due to cellular and synaptic network interactions matter. A major motivation in our approach is to solidify what should be the best “next step” to take in consideration of detailed, multi-compartment models. Although more detail can always be added, having a basis or rationale of what would make the most sense to consider next is part of what underlies our approach. The cycling involves: (1) model development, database design and simulations, (2) database building and model extraction, (3) model analysis, and (4) design examination, limitation determination and back to model development, as schematized in Fig. 1.


Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells.

Sekulić V, Lawrence JJ, Skinner FK - PLoS ONE (2014)

The cyclical ensemble modeling approach.Schematic highlighting the methodological links between stages in the ensemble modeling approach.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4215854&req=5

pone-0106567-g001: The cyclical ensemble modeling approach.Schematic highlighting the methodological links between stages in the ensemble modeling approach.
Mentions: In this paper, we apply ensemble modeling to hippocampal O-LM cells in order to consider dendritic Ih. In doing this, we propose an experiment-modeling cycling approach (Fig. 1). The benefit of ensemble modeling has been demonstrated [5]–[8]. Our intent with the cycling approach here is to take advantage of it in the context of hippocampal interneurons. Importantly, we focus on multi-compartment models to allow consideration of non-somatic properties since, experimentally, this is where the most challenging aspects lie, and where functionally relevant aspects due to cellular and synaptic network interactions matter. A major motivation in our approach is to solidify what should be the best “next step” to take in consideration of detailed, multi-compartment models. Although more detail can always be added, having a basis or rationale of what would make the most sense to consider next is part of what underlies our approach. The cycling involves: (1) model development, database design and simulations, (2) database building and model extraction, (3) model analysis, and (4) design examination, limitation determination and back to model development, as schematized in Fig. 1.

Bottom Line: 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 Ih.These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

View Article: PubMed Central - PubMed

Affiliation: Toronto Western Research Institute, University Health Network, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada.

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 (Ih). Although dendritic Ih could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of Ih 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 Ih. 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 Ih. These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

Show MeSH