To facilitate further discussion it would be very helpful if you would read the following:
1. "The NEURON simulation environment" (overviewforhbtnn2e.pdf)
"The NEURON eimulation environment"
An earlier paper with the same title, expanded from our first article in Neural Computation.
These introduce the basic concepts that underlie the design of NEURON and the implementation of models with NEURON, with an emphasis on biophysical models of cells.
2. Hines, M.L. and Carnevale, N.T. Discrete event simulation in the NEURON environment. Neurocomputing 58-60:1117-1122, 2004.
This discusses how artificial spiking cells are implemented in NEURON and introduces the event delivery system.
You might also find it helpful to read the section about NEURON in
Brette et al. Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23:349-398, 2007.
which may help you understand something about how spike driven synaptic transmission is implemented in NEURON.
All of these articles are available from links at http://www.neuron.yale.edu/neuron/nrnpubs
Spike driven synaptic transmission is implemented with events that are conveyed from a presynaptic source to a postsynaptic target by instances of the NetCon (Network Connection) class. Sources may be artificial spiking cells or biophysical model cells. NEURON's built in artificial spiking cells and synaptic mechanism may receive events from one or more presynaptic sources.
The IntFire cell classes are artificial spiking cell models (other kinds of artificial spiking cell models are also possible in NEURON). Artificial spiking cell models in NEURON are "point neurons" described by very simple equations that have analytical solutions and do not require numerical integration. They are not synaptic mechanism models. They receive events from other cells (via NetCons) and generate events that may be deliverd to other cells (via NetCons).
ExpSyn and Exp2Syn are generic conductance change synaptic mechanisms that may be attached to biophysical model cells (model cells that are implemented with one or more "section"s (think "neurite" or "a length of unbranched cable")). Other kinds of synaptic mechanisms are also possible in NEURON. All are targets of events from presynaptic sources, and the events are delivered by NetCons.
rllin wrote:By custom I mean, for example, the input spikes are from a data file.
It is easy to use such data to control the generation of spike events in NEURON.
Since I actually have no "cell," I'm not sure whether it is excitable or passive. I'm guessing excitable since I would like an affected output?
The indispensible prerequisite for any computational model is to start with a clear and explicit conceptual model. The only reason to use computational modeling is when one has a conceptual model that is too complex to allow one to draw useful inferences by intuition alone.