AMPA/NMDA and GABAa/GABAb conductance ratios
johnhkm at overhere.com.au
Wed Sep 18 12:03:26 EST 2002
Any ideas on what causes the receptor ratio shift??? Maybe silent synapses
is inhibition without inhibitory agents, a way of preventing too much
activity prior to the establishment of 'working synapses' (recalling idea
that cortex is basically over activated and needs inhibition to keep it
under control, consistent with evolutionary rise in thalamic (at least) GABA
Unfortunately God is not going to help us at all. This ng may as well
receive the last rites.
Silent Synapses in Neural Plasticity:
Harold L. Atwood1 and
J. Martin Wojtowicz1
LEARNING & MEMORY 6:542-571 © 1999 by Cold Spring Harbor Laboratory Press
A special case that occurs often in the mammalian CNS is that of
synapses lacking functional AMPA-type glutamate receptors, but
possessing NMDA-type glutamate receptors (Fig. 2C). Such synapses may
be very prevalent early in development, and decrease in occurrence
thereafter, as discussed below. Although these synapses are often referred
to as silent synapses, we think they should be termed conditionally silent
synapses, because they can express a physiological response when the
membrane is depolarized, but not when it is hyperpolarized close to the
resting potential (Fig. 3).
In summary, silent synapses possessing NMDA and not AMPA receptors,
which appear to be much more frequent in the developing nervous
system, are not likely to be truly silent. More exactly, they are
conditionally silent. Use of the term silent synapse for conditionally
synapses can lead inadvertently to a rather confusing description in
which, for example, the observed bursting activity in a developing
cortical circuit has been attributed to the activity of silent
synapses (Golshani and Jones 1999). A more useful concept of the truly
silent synapse is a more restricted one. For example, the classical Hebbian
synapse, which is ineffective until a period of combined pre- and
postsynaptic activity makes it active (Hebb 1949), would fit the stricter
definition. According to this concept, the NMDA receptor can be a
suitable trigger for the associative strengthening of such synapses by the
right combination of pre- and postsynaptic mechanisms.
Organization and regulation of proteins at synapses
Jee Hae Kim and Richard L Huganir*
Current Opinion in Cell Biology 1999, 11:248-254
still not clear whether this process is due to the activation
of pre-existing inactive AMPA receptors or to the physical
insertion of active AMPA receptors into the postsynaptic
membrane. A variety of studies have indicated, however,
that the density of receptors can dramatically vary at different
synapses and can be dynamically regulated. For
example, AMPA receptor responses in cortical neuronal
cultures are modulated in response to changes in synaptic
activity and this 'synaptic scaling' may involve modulation
of receptor density at synapses [56..]. Using recombinant
virus constructs with epitope-tagged glutamate receptor,
Lissin et al. [57..] found that the surface expression of
synaptic receptors is affected by activity in hippocampal
cultures. In cultured spinal neurons, inhibition of excitatory
synaptic transmission increased postsynaptic responses
due to increases in the metabolic half-life of AMPA receptors
which resulted in increased numbers of AMPA
receptors at synapses [58..]. Immunofluorescence and
immunogold techniques have also recently been used to
detect morphological correlates of 'silent synapses'
[59..-63..]. These results support the previous electrophysiological
results and directly demonstrate that many
excitatory synapses contain NMDA receptors but many do
not physically contain AMPA receptors.
"Matt Jones" <jonesmat at physiology.wisc.edu> wrote in message
news:b86268d4.0209171827.aa9f809 at posting.google.com...
> "Bryan Price" <jprice1661 at earthlink.net> wrote in message
news:<Wttg9.4994$Os3.351275 at newsread1.prod.itd.earthlink.net>...
> > I may be asking the wrong group with this - please redirect me if
> Since you posted it on a neuroscience group, god help us if this is
> the wrong group! But looking at the recent posts around here, god help
> us anyway.
> > I've been looking at some network models that focus on the delayed
> > of NMDA currents in addition to the quick AMPA currents in biological
> > neurons. The ranges of the conductances I have for these are
> > Excitatory
> > AMPA: g(peak) ~ 0.1 - 0.3 ns, t(peak) ~ 0.3 - 1 ms
> > NMDA: g(peak) ~ 0.05 - 0.5 ns, t(peak) ~ 5 - 50 ms
> > Inhibitory
> > GABAa: g(peak) ~ 0.4 - 1 ns, t(peak) ~ 0.2 - 1.2 ms
> > GABAb: g(peak) ~ 0.1 - 0.3 ns, t(peak) ~ 40 - 150 ms
> > [The Handbook of Brain Theory and Neural Networks (Arbib, 1995),
> > Processing" (Segev), Table 1]
> Those numbers look roughly OK to me. Obviously these values will vary
> a lot between cell types, with temperature, etc. You should check
> whether the authors are quoting values for miniature- synaptic
> currents (i.e., due to release of a single vesicle) or rather in
> response to an action potential which invades -all- of the axon
> terminals at once. In that case, the effective synaptic conductance
> will be the sum of several miniature conductances.
> > 1) Easy question: What is an 'ns'? It's not just 1/giga-ohms, is it?
> A "ns" (should be "nS") is a nanoSiemen. 1 Siemen = 1/Ohm, so yes, 1
> nS = 1/1GOhm.
> > 2) Harder question: I'm trying to calculate the range of ratios between
> > AMPA and NMDA (and GABAa and GABAb) time-summed currents. I am, of
> > applying a membrane decay convolution function to the beta functions I'm
> > using to approximate the currents, with Tm ~ 7 - 50 ms.
> I'm not sure what you mean by membrane decay convolution. Is this in
> order to convert the conductance into a current under conditions of
> changing membrane potential? If so, I think I would agree with
> Christian Wilms' post that the best thing would be to explicitly
> simulate the currents and membrane potential, including
> voltage-dependence of NMDA (but this gets costly of course). If it is
> really currents you're interested in, these will always depend on
> membrane potential, which will be fluctuating in complex ways during a
> network simulation.
> If you're trying to simplify the complicated current waveforms into
> simple ratios, so that you can use a "mean field" approximation in
> your modeling, then at the very least I would explicitly simulate the
> changing NMDA current in response to a randomly fluctuating voltage,
> and average and convolve over that instead of the raw beta function.
> It would be a big mistake to ignore the voltage dependence of NMDA
> But maybe I'm not understanding the problem correctly.
> Clearly I could use
> > the maximum AMPA and the minimum NMDA conductances for one limit of the
> > ratios, and the opposite conductances for the other limit, as this would
> > inclusive.
> This actually seems pretty reasonable to me.
> However, I suspect that individual synapses (containing both AMPA
> > and NMDA receptors) maintain some sort of relationship between the
> > of each type of receptor -
> Right, this seems reasonable too. For a long time, there were some
> "average ratios" that people could depend on. Try looking up papers
> from about 10 years ago that have both 1) "Rosenmund" and "Westbrook"
> or 2) "Bekkers" and "Stevens" on the authorlist.
> Unfortunately, these days, these "average ratios" are no longer in
> vogue, thanks to a recent phenomenon known as "silent synapses". So
> you're following statement is no longer as accurate as it was 5 years
> I don't believe that a synapse would have no AMPA
> > receptors while also having a plethora of NMDA receptors, or vice-versa.
> > Does anyone know of research that might reveal such a relationship?
> See the authors I mentioned above for evidence in support of such a
> Do a medline search for "silent synapses" to get a more recent view in
> which the ratio of AMPA/NMDA is in almost perpetual flux. Silent
> synapses refers to a form of plasticity in which this ratio is the key
> element in the learning rule for adjustment of synaptic weight, to put
> it in neural net-speak.
> Good luck,
> > Thanks you,
> > Bryan Price
> > [ Also posted to comp.ai.neural-nets ]
More information about the Neur-sci