Complex Parameter Landscape for a Complex Neuron Model
By P. Achard, E. Schutter. 2006
280 citations recorded by the source corpus.
The electrical activity of a neuron is strongly dependent on the ionic channels present in its membrane. Modifying the maximal conductances from these channels can have a dramatic impact on neuron behavior. But the effect of such modifications can also be cancelled out by compensatory mechanisms among different channels. We used an evolution strategy with a fitness function based on phase-plane analysis to obtain 20 very different computational models of the cerebellar Purkinje cell. All these models produced very similar outputs to current injections, including tiny details of the complex firing pattern. These models were not completely isolated in the parameter space, but neither did they belong to a large continuum of good models that would exist if weak compensations between channels were sufficient. The parameter landscape of good models can best be described as a set of loosely connected hyperplanes. Our method is efficient in finding good models in this complex landscape. Unraveling the landscape is an important step towards the understanding of functional homeostasis of neurons.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Statistical and Nonlinear Physics
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
- Idan Segev
- Felix Schürmann
- B. Bean
- Henry Markram
- Shaul Druckmann
- Eve Marder
- Wulfram Gerstner
- Alexander Borst
In Cognitive Neuroscience
- Idan Segev
- Felix Schürmann
- B. Bean
- Shaul Druckmann
- Henry Markram
- Eve Marder
- Alexander Borst
- Wulfram Gerstner
In Statistical and Nonlinear Physics
Citations in the corpus
- Variability, compensation, and modulation in neurons and circuits (2011, background): “…in many cases, to construct families of models rather than single highly tuned models to capture the behavior of individual neurons and circuits (4 – 8, 12, 13, 22, 62, 63), and ( ii ) the importance of trying to measure as many system components as possible within an individual is now evident.”
- The action potential in mammalian central neurons (2007, background): “Because of these features, the system is ideally suited for mathematical modelling, probably more so than any other biological system, and relatively detailed models (including at least approximate kinetic descriptions of up to 11 different voltage-dependent conductances) have been formulated for th”
- A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data (2007, methodology): “Since the target data traces themselves are variable and selecting but one of the traces must to some extent be arbitrary, a direct trace to trace comparison between single traces might not serve as the best method of comparison between experiment and model.”