Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties

By Etay Hay, Sean L. Hill, Felix Schürmann, Henry Markram, Idan Segev. PLoS Comput. Biol., 2011

365 citations recorded by the source corpus.

The thick-tufted layer 5b pyramidal cell extends its dendritic tree to all six layers of the mammalian neocortex and serves as a major building block for the cortical column. L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. This automated framework can be used to develop a database of faithful models for other neuron types. The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/e58af0fed5a0cec8eb042c7253abf6bfc134e9ba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons (2016, methodology): “Finally, we also demonstrate that our main results generalize to an anatomically reconstructed and physiologically detailed L5 neuron model [23].”
Flexible statistical inference for mechanistic models of neural dynamics (2017, background): “Biophysical neuron models are typically fit to data with genetic algorithms applied to the distance between simulated and measured data-features [2, 8, 9, 46].”
The use and abuse of large-scale brain models. (2014, neutral): “Each neuron and synapse is simulated in a great deal of detail, taking into account ion channel composition, spatial morphology and detailed physiological data [14].”
Reconstruction and Simulation of Neocortical Microcircuitry. (2015, methodology): “Reconstructing Neuronal Physiology A series of algorithms and an automated workflow were developed to configure NEURON models to reproduce the electrophysiology of each me-type, (Druckmann et al., 2007, 2011; Hay et al., 2011) (see Experimental Procedures).”