Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and
plasticity of their synapses is thought to underlie learning in the brain. However, such long-
term synaptic changes have been experimentally characterized between only a few types of
PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we
introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium
dynamics, and show in a neocortical microcircuit model that a single parameter set is suf-
ficient to unify the available experimental findings on long-term potentiation (LTP) and long-
term depression (LTD) of PC connections. In particular, we find that the diverse plasticity
outcomes across the different PC types can be explained by cell-type-specific synaptic
physiology, cell morphology and innervation patterns, without requiring type-specific plas-
ticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict
qualitatively different plasticity dynamics from those observed in vitro. This work provides a
first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an
open framework for further developing models of cortical synaptic plasticity.
As a secondary result, they also found that in-vitro experiments often use an unrealistically high extracellular calcium concentration, and they discuss the implications:
Plasticity at physiological calcium conditions. Extracellular
calcium concentration is an important modulator of synaptic
transmission and calcium currents. Calcium levels in vivo are
significantly lower than the conditions of plasticity experiments
in vitro considered in this work to constrain and test the model
(in vivo: 1 to 1.3 mM; in vitro: 2 mM or higher  ). Given the
central role of calcium for plasticity, it is important to take into
account the impact of physiological calcium concentration to
understand the learning rules which are operating in vivo.
Our predictions of plasticity at in vivo levels of calcium high-
lighted major qualitative differences with respect to in vitro
conditions, as also suggested by a recent experimental study in the
hippocampus  . While we accounted for the effects of reducing
extracellular calcium on multiple components of the synapse
model (i.e. calcium driving force, plasticity thresholds and
NMDAR fractional calcium current), these results are mostly
due to the estimated five-fold decrease in synaptic release
probability  . Under these conditions, successful pairing events
become rare, suggesting an important role for N-methyl-D-
aspartate (NMDA) spikes and other dendritic nonlinearities
for evoking sufficiently large calcium influxes to induce
plasticity [71–73] . The proposed in silico framework could be used to
study these dynamics without major modifications, for example
by simulating the scenario where multiple presynaptic neurons
activate neighboring synapses. In this scenario, synapses could
cooperate through voltage nonlinearities to evoke calcium tran-
sients of sufficient magnitude to induce learning in a single trial
(one-shot learning [74,75] ), circumventing the high failure rate.
So, previous PC models were unrealistic, including all those Blue Brain / HBP cortical column simulations? They couldn’t tell it by comparing simulations with in vivo brain observations? And now they can redo all that based on these new models?
Do you think these new models account for macro-data that wasn’t available during this study, for example that dendritic learning stuff we discussed lately?
I mean the in-vitro experiments weren’t totally unrealistic, they just had stronger synapses than they should have.
They did not mention dendritic learning in the article.
According to Synaptic plasticity rules with physiological calcium levels - PMC, in vitro STDP studies use higher calcium concentration “because elevated calcium is known to stabilize recording of synaptic transmission and to avoid intrinsic bursting that could obscure induction of STDP with single pre- and postsynaptic spikes”.
Well, do they correctly account for this excessive calcium when translating the results into models of in-vivo behaviour?
I don’t know. The plasticity rules change with the real calcium levels, so probably not as far as plasticity goes. Maybe it’s possible to account for it for activity, I don’t know how neurons work well enough.