This is a simple state-variable model for repetitive firing in neurons. It is very simple--for example, the neuron is just a point. It fires a spike of constant amplitude and width when threshold is reached, and the spike is followed by activation of a potassium conductance. Although it is simple, it correctly reproduces many features of repetitive neuronal firing. Some interesting parameters in this model are:
These examples show some characteristics of the model. Start with the default parameters and go through the steps in order. You can use Reset in the parameter dialog to get the default values back.
1. Firing ceases ("accomodates") with long stimuli.
2. It continues without letup if firing threshold is unchanged.
3. Now change threshold sensitivity back to its original value.
4. And see that a long time constant for accomodation has a similar effect.
5. This can be partly overcome by increasing the K sensitivity.
6. Or by lengthening the time constant of the K sensitivity.
7. And making the K reversal potential more negative also affects repetitive firing.