In article <1991Nov1.195125.18344 at midway.uchicago.edu> bsg1 at quads.uchicago.edu (Ben Gerber) writes:
>>I have posted a message similar to this one a few months ago, and didn't
>receive a strong response (make that no response). I'd like to try it
>again, with the plea of receiving e-mail from anybody having any
>information, comments, questions, thoughts, etc. about the matter.
>>My interest is this: for studied neurons, what are typical channel
>densities of post-synaptic connections (both excitatory and inhibitory)? I've
>heard that often there is a larger inhibitory channel density
>(data possibly expressed as conductance per unit area of patch of membrane)
>than excitatory. Any ideas about the number of channels (excitatory or
>inhibitory) for a given nerve cell?
>>>I'm interested in ANY information (references, etc.). Thanks in advance.
>> - Ben
>Ben Gerber /\ "Academia is, at times, a very small
>University of Chicago /\ world for good and ill."
>bsg1 at quads.uchicago.edu /\ - Henny Graupe
If you are looking for specific information, you might try narrowing the
field of your question. For example, what TYPE of nerve cells are you
looking for the densities of? Crayfish Motor Giants? CA1 Hippocampal?
Part of the complexity that one sees in the firing behavior in the CNS is
due to the uniqueness of synaptic distribution. The averages that you are
looking for will be cell type and species specific.
Data is classified by transmitter subtype. The most popular method is using
receptor-binding studies. You find a substance that irreversibly binds to
the post-S sites, radiolabel it, dump it on your sample, and measure the
conc of radiolabel. There is not a universal inhib or excite indicator.
Adding to this confusion, is the fact that some transmiters can be excitatory
in some systems and inhibitory in others. To generate an answer to your
question, one would need to compare the electrophys data to the receptor-
binding data for a particular cell type. Additionally, one hopes that all
the major transmitter types have been tested, and that data is available
for the system in question.
I would begin by choosing a cell type with lots of data available. Then
use a database like medline to search for all the recptor characterization
I share your frustration! If understanding the CNS were only that simple....!
Rogene M. Eichler
Neuroscience Program Rogene at neuro.med.umn.edu
U of Minnesota Eichler at s1.msi.umn.edu
Minnesota Supercomputer Institute Eichler at a1.arc.umn.edu