Dendritic properties control energy efficiency of APs in cortical pyramidal cells (Yi et al 2017)

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Accession:230329
Neural computation is performed by transforming input signals into sequences of action potentials (APs), which is metabolically expensive and limited by the energy available to the brain. The energy efficiency of single AP has important consequences for the computational power of the cell, which is determined by its biophysical properties and morphologies. Here we adopt biophysically-based two-compartment models to investigate how dendrites affect energy efficiency of APs in cortical pyramidal neurons. We measure the Na+ entry during the spike and examine how it is efficiently used for generating AP depolarization. We show that increasing the proportion of dendritic area or coupling conductance between two chambers decreases Na+ entry efficiency of somatic AP. Activating inward Ca2+ current in dendrites results in dendritic spike, which increases AP efficiency. Activating Ca2+-activated outward K+ current in dendrites, however, decreases Na+ entry efficiency. We demonstrate that the active and passive dendrites take effects by altering the overlap between Na+ influx and internal current flowing from soma to dendrite. We explain a fundamental link between dendritic properties and AP efficiency, which is essential to interpret how neural computation consumes metabolic energy and how the biophysics and morphology contributes to such consumption.
Reference:
1 . Yi G, Wang J, Wei X, Deng B (2017) Dendritic properties control energy efficiency of action potentials in cortical pyramidal cells. Frontiers in Cellular Neuroscience
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s): I Sodium; I Potassium; I Calcium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Action Potentials; Action Potential Initiation; Active Dendrites; Dendritic Action Potentials;
Implementer(s):
Search NeuronDB for information about:  I Sodium; I Calcium; I Potassium;
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Yi et al 2017
model I
model II
model III
readme.txt
                            
This is the readme for the models associated with the paper:

Guosheng Yi,Jiang Wang, Xile Wei, Bin Deng (2017) Dendritic properties control energy efficiency of action potentials in cortical pyramidal cells. Frontiers in Cellular Neuroscience

The matlab code is contributed by Guosheng Yi.

How to compile the files:
(1) Download and uncompress the files into the same directory.
(2) Open Matlab and go to that directory.

Usage:
(1) Model I. There are no active currents in its dendritic chamber. 
* Set Id=3, gc=0.5, and p=0.2, 0.4, 0.6, 0.8. Run ML_two_ode.m in this directory to reproduce the spike trains in Figure 1B.
* Set Id=2, p=0.5, and gc=0.2, 0.8, 1.6, 2.4. Run ML_two_ode.m in this directory to reproduce the spike trains in Figure 3B.
(2) Model II. There is only a Ca2+ current in its dendrite.Set Id=5, gc=0.3, and p=0.4, 0.6. Run ML_two_ode.m in this directory to reproduce Figure 6B.
(3) Model III. There is an inward Ca2+ current and an outward Ca2+-activated K+ current in its dendrite. Set Id=2, gc=0.6, and p=0.4, 0.6. Run ML_two_ode.m in this directory to reproduce Figure 9B.

ML_two_ode.m in each directory also includes the code for calculating the ionic currents underlying the spike train. If the users integrate Na+ current over the relevant duration for a given action potential, you will obtain total Na+ load, overlap Na+ load, and minimum Na+ load of the spike.

Thanks for your interest in these models.

Guosheng Yi, Jiang Wang and Xile Wei
School of Electrical and Information Engineering
Tianjin University, Tianjin 300072, China.
xilewei@tju.edu.cn





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