pre-Bötzinger complex variability (Fietkiewicz et al. 2016)

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Accession:184732
" ... Based on experimental observations, we developed a computational model that can be embedded in more comprehensive models of respiratory and cardiovascular autonomic control. Our simulation results successfully reproduce the variability we observed experimentally. The in silico model suggests that age-dependent variability may be due to a developmental increase in mean synaptic conductance between preBötC neurons. We also used simulations to explore the effects of stochastic spiking in sensory relay neurons. Our results suggest that stochastic spiking may actually stabilize modulation of both respiratory rate and its variability when the rate changes due to physiological demand. "
Reference:
1 . Fietkiewicz C, Shafer GO, Platt EA, Wilson CG (2016) Variability in respiratory rhythm generation: In vitro and in silico models Communications in Nonlinear Science and Numerical Simulation 32:158-168
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Respiratory column neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Noise Sensitivity; Development;
Implementer(s):
/* Use run.hoc to start simulation. Creates a raster plot similar to Figure 2C in "Variability in respiratory rhythm generation: in vitro and in silico models", C Fietkiewicz, GO Shafer, EA Platt, CG Wilson, Communications in Nonlinear Science and Numerical Simulation, Volume 32, March 2016, Pages 158–168 (doi: 10.1016/j.cnsns.2015.08.018).
NOTE: To get more bursts, increase the simulation time.
Author contact: chris.fietkiewicz@case.edu
Author website: http://filer.case.edu/cxf47
*/

strdef suffix
simulationTime = 2000 // Total simulation time in msec
suffix = "test" // Output file suffix (if outputMode is set to 1)
outputMode = 1.00000000 // (0 = file, 1 = plot)
seed = 2.00000000 // Seed for random number generators (except T cell start time)
tonicToPMweight = 0.00025000 // Mean synaptic weight from T cells to PM cells
tonicToPMprob = 0.40000000 // Connection probability from T cells to PM cells
PMtoPMweight = 0.00003000 // Mean synaptic weight from PM cells to PM cells
PMtoPMprob = 0.57000000 // Connection probability from PM cells to PM cells
tonicNoise = 1.00000000 // Noise level for T cells
tonicToPMrange = 0.10000000 // Multiplier that sets min/max synaptic weight from T cells to PM cells [min = tonicToPMweight*(1-tonicToPMrange), max = tonicToPMweight*(1+tonicToPMrange]
PMtoPMrange = 0.000010 // Variance for normally distributed synaptic weight from PM cells to PM cells
tonicSeed = 1.00000000 // Seed for random number generators for T cell start time
numTonic = 100.00000000 // Quantity of T cells
numPM = 100.00000000 // Quantity of PM cells
tonicPeriodMean = 700 // Mean spike rate for T cells
setEK = -85.00000000 // K+ reversal potential
setEleak = -73.00000000 // Leak reversal potential
load_file("initialize.hoc")
initialize(simulationTime, suffix, outputMode, seed, tonicToPMweight, tonicToPMprob, PMtoPMweight, PMtoPMprob, tonicNoise, tonicToPMrange, PMtoPMrange, tonicSeed, tonicPeriodrange, numTonic, numPM, tonicPeriodMean, setEK, setEleak)
load_file("main.hoc")