Name
Title | ||
|---|---|---|
Models Of Neocortical Layer 5b Pyramidal Cells Capturing A Wide Range Of Dendritic And Perisomatic Active Properties |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1371/journal.pcbi.1002107 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
action potential,multi objective optimization,ion channel,evolutionary algorithm,ion channels,action potentials,back propagation,evolutionary algorithms,optimization | Neocortex,Neuroscience,Biology,Cortical column,Artificial intelligence,Single-cell analysis,Ion channel,Neuron,Pyramidal cell,Dendritic spike,Soma,Genetics,Machine learning | Journal |
Volume | Issue | ISSN |
7 | 7 | 1553-7358 |
Citations | PageRank | References |
40 | 2.30 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Etay Hay | 1 | 40 | 2.30 |
| Sean Hill | 2 | 83 | 7.22 |
| Felix Schürmann | 3 | 245 | 27.04 |
| Henry Markram | 4 | 1620 | 199.38 |
| Idan Segev | 5 | 153 | 27.18 |