Sde Tutorial #2044
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Sde Tutorial #2044
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schmitts
reviewed
Nov 28, 2022
schmitts
reviewed
Nov 28, 2022
schmitts
reviewed
Nov 28, 2022
schmitts
reviewed
Nov 28, 2022
schmitts
reviewed
Nov 28, 2022
doc/tutorial/calcium_stdp_curve.rst
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| SOLVE state METHOD stochastic | ||
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| This is sufficient to inform Arbor about the stochasticity of the mechanism. |
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can we add a link to an explanation of what kind of solver stochastic implements? https://docs.arbor-sim.org/en/latest/dev/numerics.html
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Yes you are right, that should be mentioned. I added links to the longer explanations of how we treat SDEs but also added an entry to the dev/numerics.rst - this document was actually commented out, so I re-added it to the TOC.
schmitts
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Nov 28, 2022
schmitts
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Nov 28, 2022
doc/tutorial/calcium_stdp_curve.rst
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| statistically meaningful result. The number of simulations per case is given by the ensemble size | ||
| and the initial conditions. In our case, we have to inital states, :math:`\rho(0)=0` and | ||
| :math:`\rho(0)=1`, and for each initial state we want to run :math:`100` simulations. This can | ||
| easiest be achieved by placing :math:`100` synapses per initial state onto the cell. |
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maybe add ... and allows to parallelize?
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true, added some more explanation
schmitts
reviewed
Nov 28, 2022
| The pre- and postsynaptic events are generated at explicit schedules, where the presynaptic event | ||
| is shifted in time by :math:`D -\text{time lag}` with respect to the presynaptic event, which in | ||
| turn is generated regularly with the frequency :math:`f`. The postsynaptic events are driven by the | ||
| deterministic synapse with weight `1.0`, while the presynaptic events are generated at the |
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maybe add: arbitrary, but large, weight ...?
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ok, expanded explanation
schmitts
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Nov 28, 2022
schmitts
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Nov 28, 2022
brenthuisman
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Dec 12, 2022
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Implementation of stochastic calcium plasticity curves, originally authored by Sebastian Schmitt @schmitts, and adapted here for Arbor inclusion.
Closes #1987