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date: 31 October 2020

Single Neuron Computational Modelinglocked

  • Yeonjoo YooYeonjoo YooBaylor University, College of Medicine
  •  and Fabrizio GabbianiFabrizio GabbianiBaylor University, College of Medicine

Summary

Computational modeling is essential to understand how the complex dendritic structure and membrane properties of a neuron process input signals to generate output signals. Compartmental models describe how inputs, such as synaptic currents, affect a neuron’s membrane potential and produce outputs, such as action potentials, by converting membrane properties into the components of an electrical circuit. The simplest such model consists of a single compartment with a leakage conductance which represents a neuron having spatially uniform membrane potential and a constant conductance summarizing the combined effect of every ion flowing across the neuron’s membrane. The Hodgkin-Huxley model introduces two additional active channels; the sodium channel and the delayed rectifier potassium channel whose associated conductances change depending on the membrane potential and that are described by an additional set of three nonlinear differential equations. Since its conception in 1952, many kinds of active channels have been discovered with a variety of characteristics that can successfully be modeled within the same framework. As the membrane potential varies spatially in a neuron, the next refinement consists in describing a neuron as an electric cable to account for membrane potential attenuation and signal propagation along dendritic or axonal processes. A discrete version of the cable equation results in compartments with possibly different properties, such as different types of ion channels or spatially varying maximum conductances to model changes in channel densities. Branching neural processes such as dendrites can be modeled with the cable equation by considering the junctions of cables with different radii and electrical properties. Single neuron computational models are used to investigate a variety of topics and reveal insights that cannot be evidenced directly by experimental observation. Studies on action potential initiation and on synaptic integration provide prototypical examples illustrating why computational models are essential. Modeling action potential initiation constrains the localization and density of channels required to reproduce experimental observations, while modeling synaptic integration sheds light on the interaction between the morphological and physiological characteristics of dendrites. Finally, reduced compartmental models demonstrate how a simplified morphological structure supplemented by a small number of ion channel-related variables can provide clear explanations about complex intracellular membrane potential dynamics.

Subjects

  • Sensory and Motor Systems

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