Digital Neurons

The simulations from which you can choose are listed below in an order roughly approximating their level of complexity:

Uniform membrane Patches

• Patch of Hodgkin & Huxley membrane: Action Potential (AP) and Voltage Clamp (VC)
• Insert new channels and mechanisms: Compare AP shapes, VC currents

Voltage and current Distributions in Axons

• Passive membranes (no voltage-sensitive channels): Signals decay rapidly with distance
• Squid giant axon (HH channels): Propagation of impulses
• Changes in Diameter: Explore branch-to-axon and axon-to-branch propagation

Myelinated nerve fibers Transmission in:

• Normal uniformly myelinated fibers: Investigate how the impulse velocity depends on nodal and internodal parameters.
• Normal-to-demyelinated region (Multiple Sclerosis): Look for several ways to restore transmission through this transition.
• Myelin-to-nerve terminal for a neuromuscular junction

Neuromuscular junctions: Differences between frog and mammalian junctions

Intracellular Ca and transmitter release: Intracellular binding, diffusion, and a simple schema for release

Simple Cells: Dendrites, soma, & axons

Synaptic integration:Temporal and spatial

Simple network of cells

All of the sample applications available here run properly on a unix machine but the MS Windows 3.1 version faces the difficulty of the variety of screen resolutions used on PCs. When I have run these simulations on different PCs, I found that the locations and sizes of panels and windows vary a lot. This is probably due to the variety of video cards and drivers. The examples included here have been set up on a PC with a 1024 x 768 resolution. Nevertheless, some rescaling by the user may be required even on PCs running Windows with this resolution. To deal with this problem, Mike Hines has built in an additional control under "Other" in the Manager panel called "virtual screen". You can resize by choosing among the submenu offerings when "Scale" is selected. For repetitive use (as in a classroom situation) an alternative method might be considered. Because of its great potential of this unique learning tool, I am planning to expand this chapter into a separate volume. For this purpose, I would especially appreciate suggestions for enhancements. Please send them to moore@neuro.duke.edu