Add numeric solver to synapses #1208
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Bencher Report| Branch | 1208/merge |
| Testbed | ubuntu-latest |
Click to view all benchmark results
| Benchmark | Latency | Benchmark Result seconds (s) (Result Δ%) | Upper Boundary seconds (s) (Limit %) |
|---|---|---|---|
| tests/nest_continuous_benchmarking/test_nest_continuous_benchmarking.py::TestNESTContinuousBenchmarking::test_stdp_nn_synapse | 📈 view plot 🚷 view threshold | 3.75 s(+9.52%)Baseline: 3.43 s | 3.77 s (99.56%) |
| Benchmark | Latency | Benchmark Result seconds (s) (Result Δ%) | Upper Boundary seconds (s) (Limit %) |
|---|---|---|---|
| tests/nest_continuous_benchmarking/test_nest_continuous_benchmarking.py::TestNESTContinuousBenchmarking::test_stdp_nn_synapse | 📈 view plot 🚷 view threshold | 3.19 s(-6.62%)Baseline: 3.42 s | 3.76 s (84.89%) |
| Benchmark | Latency | Benchmark Result seconds (s) (Result Δ%) | Upper Boundary seconds (s) (Limit %) |
|---|---|---|---|
| tests/nest_continuous_benchmarking/test_nest_continuous_benchmarking.py::TestNESTContinuousBenchmarking::test_stdp_nn_synapse | 📈 view plot 🚷 view threshold | 3.16 s(-7.39%)Baseline: 3.42 s | 3.76 s (84.19%) |
clinssen
requested changes
Aug 18, 2025
| y += [syn_stats["y"]] | ||
| z += [syn_stats["z"]] | ||
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| # TODO: Adjust tolerance |
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Could you take care of this TODO?
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The tolerance is a bit high. The tests fail if I decrease the tolerance. I tried a few things, such as adjusting rtol and atol, but couldn't lower the difference. Do you have any ideas?
pynestml/codegeneration/resources_nest/point_neuron/common/SynapseHeader.h.jinja2
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| @@ -294,7 +294,7 @@ def transform_neuron_synapse_pair_(self, neuron: ASTModel, synapse: ASTModel): | |||
| strictly_synaptic_vars = ["t"] # "seed" this with the predefined variable t | |||
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| if self.option_exists("strictly_synaptic_vars") and removesuffix(synapse.get_name(), FrontendConfiguration.suffix) in self.get_option("strictly_synaptic_vars").keys() and self.get_option("strictly_synaptic_vars")[removesuffix(synapse.get_name(), FrontendConfiguration.suffix)]: | |||
| strictly_synaptic_vars.append(self.get_option("strictly_synaptic_vars")[removesuffix(synapse.get_name(), FrontendConfiguration.suffix)]) | |||
| strictly_synaptic_vars.extend(self.get_option("strictly_synaptic_vars")[removesuffix(synapse.get_name(), FrontendConfiguration.suffix)]) | |||
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This was a bug, right? How could this ever have worked before?
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The synapse model used in the test in this PR has variables that strictly belong to the synapse. Hence, I added them to strictly_synaptic_vars in the codegen_opts introduced in #1229.
This then caused the append in the line to create a list of lists, which failed at line:
nestml/pynestml/utils/ast_utils.py
Line 1779 in 81ed384
That's why I changed it to
extend. Perhaps we should change all the append calls here to extend instead to avoid errors.
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Could you also update |
clinssen
reviewed
Sep 11, 2025
| @@ -25,7 +25,7 @@ Event-based updating of synapses | |||
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| The synapse is allowed to contain an ``update`` block. Statements in the ``update`` block are executed whenever the internal state of the synapse is updated from one timepoint to the next; these updates are typically triggered by incoming spikes. The NESTML ``timestep()`` function will return the time that has elapsed since the last event was handled. | |||
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| Synapses in NEST are not allowed to have any nonlinear time-based internal dynamics (ODEs). This is due to the fact that synapses are, unlike nodes, not updated on a regular time grid. Linear ODEs are allowed, because they admit an analytical solution, which can be updated in a single step from the previous event time to the current event time. However, nonlinear dynamics are not allowed because they would require a numeric solver evaluating the dynamics on a regular time grid. | |||
| Synapses can have ODEs with linear and non-linear dynamics. In the case of linear dynamics, the ODEs are solved with the propagators provided by the ODE-toolbox; for non-linear dynamics, the ODEs are solved using a fourth order Runge-Kutta solver with adaptive timestep. | |||
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Can the tolerances be adjusted like in the neuron model? Could you add a reference to GSL or to the GSL solver?
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