Ms. Zoe Morgan

BIOLOGICAL MODELLING AND SIMULATION USING NEURON & HHSIM

INDEX
Abstract………………………………………………………………….3

1. Equilibrium Potential………………………………………………3

2. Membrane Potential………………………………………………..4

3. The Action Potential………………………………………………..5

4. The Fast Sodium Channel…………………………………………..6

5. The Delayed Rectifier………………………………………………7
6.
7. Voltage-Gated Channel Parameters…………………………………8

8. Solution to problems…...……………………………………………9
NEURON………………………………………………………………..11
1. PART A……………………………………………………………11

2. PART B……………………………………………………………27

3. PART C……………………………………………………………46

4. PART D…………………………………………………………….62

5. PART E…………………………………………………………….78
ACKNOWLEDGEMENTS…………………………………………….87
CONCLUSION…………………………………………………………88
REFERENCES………………………………………………………….89
HHSIM EXERCISES
I.
Equilibrium Potential
II.
Membrane Potential
III.
The Action Potential
IV.
The Fast Sodium Channel
V.
The Delayed Rectifier
VI.
Voltage-Gated Channel Parameters
Part I: Equilibrium Potential
We will first explore the equilibrium potential of a cell with a single channel type. Click on the Channels button in the main window to call up the Channels window. In the Channels window, turn off the all the channels except the first one (passive sodium), by clicking on their respective buttons. The buttons should change colour to grey when you turn them off. Notice that the resting potential Vr displayed in the Membrane window is now equal to the reversal potential for sodium, 52.4 mV. A mouse click on the red line will show you the exact value of Vm. Vm may not be exactly equal to Vr, but clicking the yellow Nudge button a few times will nudge things along until the voltage reaches the theoretical asymptote. Or you can click Run, wait a while, and then click Stop.
Question 1. Calculate the effect of halving the external sodium concentration. Remember, we 're assuming that only sodium channels are present. You will first need to calculate RT/zF at 6.3o, which you can do

References: HHSIM 1. Channeling with Bard, an online tutorial by G. Bard Ermentrout. 2. Biophysics of Computation: Information Processing in Single Neurons, by Christof Koch. (Chapter 6) 3. Theoretical Neuroscience, by Peter Dayan and Larry F. Abbott. (Chapter 5) 4. A Simple Sodium - Potassium Gate Model, by James K. Peterson. NEURON 1. Hines, M.L. and Carnevale, N.T. NEURON: a tool for neuroscientists. The Neuroscientist 7:123-135, 2001. 2. Carnevale, N.T. and Hines, M.L. The NEURON Book. Cambridge, UK: Cambridge University Press, 2006. 3. Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Goodman, P.H., Harris, F.C.J., Zirpe, M., Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt, M., Lansner, A., Rochel, O., Vieville, T., Muller, E., Davison, A., El Boustani, S., and Destexhe, A. Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23:349-398, 2007. 4. Cannon, R.C., Gewaltig, M.O., Gleeson, P., Bhalla, U.S., Cornelis, H., Hines, M.L., Howell, F.W., Muller, E., Stiles, J.R., Wils, S., and De Schutter, E. Interoperability of neuroscience modeling software: current status and future directions. Neuroinformatics 5:127-138, 2007