![](bg5.png)
The second order model could be decomposed
into two first order models and suggest that two
major sub-processes underlie mGluRLTD: one slow
and one fast sub-process (see Table 3). A parallel
circuit and a feedback circuit were suggested as
candidate configurations of these two sub-processes.
Possibly, the fast time constants describes the
fast processes immediately after induction mediated
by activation of the ERK/MAPK pathway and
tyrosine dephosphorylation (e.g. of GluR2) with the
tyrosine phosphatase striatal-enriched tyrosine
phosphatase (STEP) as a main player.
The slow time constant, in contrast, is likely to
reflect structural changes, for example in spine
number and morphology, that were demonstrated in
other models of synaptic plasticity to be protein-
synthesis-dependent and to occur on a time-scale of
hours (Fukazawa et al., 2003; Raymond, 2007).
Many studies show the presence of feedback loops
in cellular control systems (Mitrophanov &
Groisman, 2008). Neural mechanisms are known to
contain many non-linearities, but our modelling
results confirm other studies in which discrete-time
linear system identification techniques were
succesfully used for modelling brain signals (e.g.
Liu et al., 2003; Westwick et al., 2006; Behrend et
al., 2009).
5 CONCLUSIONS
Discrete-time TF models are interesting to
investigate mGlu receptor-dependent LTD, because
of their computational and conceptional simplicity
and since they are able to combine the advantages of
a data-based approach (accurate models) with a
mechanistic approach (meaningful parameters). This
study suggests that the dynamic data-based
modelling approach can be a valuable tool for
reverse engineering of mGluR-dependent LTD
responses. Moreover, this approach can also be
extended to other forms of LTD and LTP using
other induction protocols as input for the TF models.
It is expected that such system identification
methods can aid to unravel the complexities of
synaptic function and its role in disease.
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