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Merge pull request #771 from apdavison/multisynapse3
Add "composed" models allowing multiple post-synaptic receptor types
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| A demonstration of two different types of neuron model: | ||
| - "monolithic" (pre-defined synaptic receptor types) | ||
| - "composed" (the user can choose the synaptic receptor types) | ||
| This example shows how to build a composed model that matches the | ||
| built-in "IF_curr_exp" model, and also demonstrates one of the | ||
| additional capabilities of the composed approach, the ability to record | ||
| more of the state variables of the synapse model. | ||
| Usage: monolith_vs_composed.py [-h] [--plot-figure] [--debug DEBUG] simulator | ||
| positional arguments: | ||
| simulator neuron, nest, brian2 or another backend simulator | ||
| optional arguments: | ||
| -h, --help show this help message and exit | ||
| --plot-figure Plot the simulation results to a file. | ||
| --debug DEBUG Print debugging information | ||
| """ | ||
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| from datetime import datetime | ||
| from pyNN.parameters import Sequence | ||
| from pyNN.utility import get_simulator, init_logging, normalized_filename | ||
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| # === Configure the simulator ================================================ | ||
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| sim, options = get_simulator( | ||
| ( | ||
| "--plot-figure", | ||
| "Plot the simulation results to a file.", | ||
| {"action": "store_true"}, | ||
| ), | ||
| ("--debug", "Print debugging information"), | ||
| ) | ||
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| if options.debug: | ||
| init_logging(None, debug=True) | ||
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| sim.setup(timestep=0.1, min_delay=1.0) | ||
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| # === Build and instrument the network ======================================= | ||
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| celltype_monolith = sim.IF_curr_exp( | ||
| tau_m=10.0, v_rest=-60.0, tau_syn_E=1.0, tau_syn_I=2.0 | ||
| ) | ||
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| celltype_composed = sim.PointNeuron( | ||
| sim.LIF(tau_m=10.0, v_rest=-60.0), | ||
| excitatory=sim.CurrExpPostSynapticResponse(tau_syn=1.0), | ||
| inhibitory=sim.CurrExpPostSynapticResponse(tau_syn=2.0), | ||
| ) | ||
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| neurons_monolith = sim.Population(1, celltype_monolith, initial_values={"v": -60.0}) | ||
| neurons_composed = sim.Population(1, celltype_composed, initial_values={"v": -60.0}) | ||
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| neurons_monolith.record("v") | ||
| neurons_composed.record(["v", "excitatory.isyn", "inhibitory.isyn"]) | ||
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| neurons = neurons_monolith + neurons_composed | ||
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| inputs = sim.Population( | ||
| 2, | ||
| sim.SpikeSourceArray( | ||
| spike_times=[ | ||
| Sequence([30.0]), | ||
| Sequence([120.0]), | ||
| ] | ||
| ), | ||
| ) | ||
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| connections = { | ||
| "excitatory": sim.Projection( | ||
| inputs[0:1], | ||
| neurons, | ||
| sim.AllToAllConnector(), | ||
| synapse_type=sim.StaticSynapse(weight=0.5, delay=1.5), | ||
| receptor_type="excitatory", | ||
| label="exc", | ||
| ), | ||
| "inhibitory": sim.Projection( | ||
| inputs[1:2], | ||
| neurons, | ||
| sim.AllToAllConnector(), | ||
| synapse_type=sim.StaticSynapse(weight=-0.2, delay=1.5), | ||
| receptor_type="inhibitory", | ||
| label="inh", | ||
| ), | ||
| } | ||
| # === Run the simulation ===================================================== | ||
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| sim.run(200.0) | ||
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| # === Save the results, optionally plot a figure ============================= | ||
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| filename = "Results/monolith_vs_composed_{}.pkl".format(options.simulator) | ||
| data = neurons.get_data().segments[0] | ||
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| if options.plot_figure: | ||
| from pyNN.utility.plotting import Figure, Panel | ||
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| figure_filename = filename.replace("pkl", "png") | ||
| Figure( | ||
| Panel( | ||
| data.filter(name="v")[0], | ||
| xticks=False, | ||
| yticks=True, | ||
| ylabel="Membrane potential (mV)", | ||
| ), # ylim=(-66, -48)), | ||
| Panel( | ||
| data.filter(name="excitatory.isyn")[0], | ||
| xticks=False, | ||
| yticks=True, | ||
| ylabel="Excitatory synaptic current (nA)", | ||
| ), | ||
| Panel( | ||
| data.filter(name="inhibitory.isyn")[0], | ||
| xticks=True, | ||
| yticks=True, | ||
| ylabel="Inhibitory synaptic current (nA)", | ||
| xlabel="Time (ms)" | ||
| ), | ||
| title="Comparing 'monolithic' and 'composed' neuron models", | ||
| annotations=f"Simulated with {options.simulator.upper()} at {datetime.now().isoformat()}", | ||
| ).save(figure_filename) | ||
| print(figure_filename) | ||
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| # === Clean up and quit ======================================================== | ||
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| sim.end() |
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