Temperature-Sensitive conduction at axon branch points (Westerfield et al 1978)

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Accession:9849
Propagation of impulses through branching regions of squid axons was examined experimentally and with computer simulations. The ratio of postbranch/prebranch diameters at which propagation failed was very sensitive to temperature.
Reference:
1 . Westerfield M, Joyner RW, Moore JW (1978) Temperature-sensitive conduction failure at axon branch points. J Neurophysiol 41:1-8 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Axon;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Axonal Action Potentials; Conduction failure;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
Search NeuronDB for information about:  I Na,t; I K;
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westerfield78
README
mosinit.hoc
start.ses
                            
Westerfield, Joyner, and Moore. (1978). Temperature-sensitive conduction failure
at axon branch points. J. Neurophysiol. 41: 1-8.

This model reproduces figure 2C & 2D. An action potential, generated in the 
small (40 um) branch, fails to invade the large axon (250 um) on the first 
run at 7 degC but does invade when the temperature is reduced to 5 degC for
the second run. As discussed in the paper, the experimental values for the 
amplitude and rate of rise of the action potential were quite dependent on the
physiological condition of the axon. The same is true for the value of the 
critical temperature as can be seen in the simulation when the initial condition
(resting potential) is altered. For this simulation the resting potential
was depolarised from -65 to -64 mV as a consequence of changing the leakage
reversal potential from  el_hh = -50.183629 to -54.401079 (see the
init procedure in the mosinit.hoc file).

The original simulations were done with a Fortran program employing an 
extension of the implicit integration method of Crank and Nicholson written by
Joyner for a branching axon.

The NEURON implementation of this model was prepared by Michael Hines.
Questions about details of this implementation should be addressed to him
at michael.hines@yale.edu.


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