Table 3.
Population parameters for the three computational network models discussed
| Population Name | Size (N) | Resting Threshold (THO), mV | THO Variability, mV | Membrane Time Constant (TMEM) | Postspike Increase in GK+ (B) | Postspike GK+ Time Constant (TGK), ms | Adaptation Threshold Increase (C) | Adaptation (TTH), ms | Noise Amplitude | DC, mV |
|---|---|---|---|---|---|---|---|---|---|---|
| Second-order cough*§ | 100 | 10.0 | 1.0 | 9.0 | 20.0 | 7.0 | 0.3 | 500.0 | 0.1 | 0.0 |
| Second-order cough (insp)ठ| 100 | 10.0 | 1.0 | 9.0 | 20.0 | 7.0 | 0.9 | 600.0 | 0.1 | 0.0 |
| Second-order cough (exp)ठ| 250 | 10 | 1.0 | 9.0 | 20.0 | 7.0 | 0.9 | 600.0 | 0.1 | 0.0 |
| E-Aug late (#2)‡ | 600 | 10.0 | 1.0 | 9.0 | 27 | 2.5 | 0 | 250 | 0.1 | 27.0 |
| Second-order cough feed-forward inhibition†‡§ | 100 | 10.0 | 1.0 | 9.0 | 20.0 | 7.0 | 0.9 | 600.0 | 0.1 | 0.0 |
Symbols indicate neuronal populations added to the base model from Poliaček et al. (48) to create version 1 (†) and version 2 (‡) of the model and populations present in the base model (48) and version 1 that were removed from version 2 (
). Variable names used by MacGregor (30) are in parentheses. All values representing voltages are relative to the resting potential, which is considered equal to zero. N is the number of neurons simulated in each population. THO, the resting threshold, is normally distributed in the population around the value of THO with a SD equal to the “THO variability” value. TMEM is the membrane time constant. B is the amplitude of the postspike increase in potassium conductance. TGK is the time constant of the potassium conductance decay following an action potential. C and TTH define the change in threshold associated with spike adaptation. C is the ratio of the threshold increase to the membrane potential increase; its value is between 0 and 1. TTH is the time constant of the rise in threshold with spike adaptation. Noise amplitude: Each cell has an internal noise generator that acts like two synapses, one with an equilibrium potential of 70 mV above resting and the other with −70 mV. Each acts as though it has an incoming firing probability of 0.05 per time step and a synapse time constant of 1.5 ms. This parameter is the conductance that gets added to the synapse conductance on each (virtual) spike. DC: An injected current will raise the membrane potential by an amount that is inversely proportional to the membrane conductance. Instead of being specified directly as a current, this parameter is specified in mV, and it is interpreted as the current that is required to raise the membrane potential by the specified number of mV when the membrane conductance has its resting value. The effect on the membrane potential at other membrane conductances will be inversely proportional to the conductance. Note also that as in other types of integrate-and-fire (IF) neuron models, our neuron models do not actually generate action potential-like spikes but only identified moments of spikes, so “spiking” shown in all neuron simulations is represented graphically by assigning vertical spikelike lines at computed times of threshold crossing.
Neuron populations that relay perturbations of the network model. A fiber population consisting of 100 fibers, each with 100 excitatory synaptic terminals and a firing probability of 0.07 at each simulation time step, was used to represent cough receptor excitation. This fiber population excited the second-order cough and feed-forward inhibitory neuron populations (synaptic strengths, 0.12 and 0.04, respectively) and the I-Driver/Plateau population (synaptic strength, 0.02).