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* development: Add clickable image to quick start page Regenerated with latest nmllite Use latest nml libraries; Regenerated multiple nml files Adding generated mdf files and images for pynn based examples Specify py version for actions/setup-python@v4 One more gha action version update Update versions github actions Latests tests More tests More tests on pynn examples Improved handling of spike transfers Updates to test pynn hh Formatted Initial tests NeuroMLlite->PyNN->MDF type info in input ports in generated graphs Fix issue with onnx trying to add int to float Regenerated with latest nml libraries Test latest modelspec
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,202 @@ | ||
| from neuromllite import ( | ||
| Network, | ||
| Cell, | ||
| InputSource, | ||
| Population, | ||
| Synapse, | ||
| RectangularRegion, | ||
| RandomLayout, | ||
| ) | ||
| from neuromllite import Projection, RandomConnectivity, Input, Simulation | ||
| import sys | ||
|
|
||
|
|
||
| def generate( | ||
| ref, | ||
| num_pop0=2, | ||
| num_pop1=2, | ||
| conn_prob=1, | ||
| conn_weight=0.3, | ||
| input_percentage=50, | ||
| input_weight=".8", | ||
| pynn_cell="IF_curr_alpha", | ||
| ): | ||
|
|
||
| ################################################################################ | ||
| ### Build new network | ||
|
|
||
| net = Network(id=ref) | ||
| net.notes = "Example: %s" % ref | ||
| net.parameters = {"input_amp": 0.99} | ||
|
|
||
| cell = Cell(id="testcell", pynn_cell=pynn_cell) | ||
|
|
||
| cell.parameters = {"i_offset": 0.0} | ||
| if "IF_" in pynn_cell: | ||
| cell.parameters["tau_refrac"] = 5 | ||
| else: | ||
| cell.parameters = {"i_offset": 0.05} | ||
| net.parameters = {"input_amp": 0} | ||
| net.cells.append(cell) | ||
|
|
||
| input_source = InputSource( | ||
| id="i_clamp", | ||
| pynn_input="DCSource", | ||
| parameters={"amplitude": "input_amp", "start": 200.0, "stop": 800.0}, | ||
| ) | ||
| net.input_sources.append(input_source) | ||
|
|
||
| r1 = RectangularRegion( | ||
| id="region1", x=0, y=0, z=0, width=1000, height=100, depth=1000 | ||
| ) | ||
| net.regions.append(r1) | ||
|
|
||
| p0 = Population( | ||
| id="pop0", | ||
| size=num_pop0, | ||
| component=cell.id, | ||
| properties={"color": "1 0 0", "radius": 20}, | ||
| random_layout=RandomLayout(region=r1.id), | ||
| ) | ||
| net.populations.append(p0) | ||
|
|
||
| if num_pop1 > 0: | ||
| p1 = Population( | ||
| id="pop1", | ||
| size=num_pop1, | ||
| component=cell.id, | ||
| properties={"color": "0 1 0", "radius": 20}, | ||
| random_layout=RandomLayout(region=r1.id), | ||
| ) | ||
| net.populations.append(p1) | ||
|
|
||
| """p2 = Population( | ||
| id="pop2", | ||
| size=1, | ||
| component=cell2.id, | ||
| properties={"color": "0 0 1", "radius": 20}, | ||
| random_layout=RandomLayout(region=r1.id), | ||
| ) | ||
| net.populations.append(p2)""" | ||
|
|
||
| net.synapses.append( | ||
| Synapse( | ||
| id="ampaSyn", | ||
| pynn_receptor_type="excitatory", | ||
| pynn_synapse_type="curr_alpha", | ||
| parameters={"tau_syn": 20}, | ||
| ) | ||
| ) | ||
| """net.synapses.append( | ||
| Synapse( | ||
| id="gabaSyn", | ||
| pynn_receptor_type="inhibitory", | ||
| pynn_synapse_type="cond_alpha", | ||
| parameters={"e_rev": -80, "tau_syn": 10}, | ||
| ) | ||
| )""" | ||
|
|
||
| if num_pop1 > 0: | ||
| net.projections.append( | ||
| Projection( | ||
| id="proj0", | ||
| presynaptic=p0.id, | ||
| postsynaptic=p1.id, | ||
| synapse="ampaSyn", | ||
| delay=2, | ||
| weight=conn_weight, | ||
| ) | ||
| ) | ||
| net.projections[0].random_connectivity = RandomConnectivity( | ||
| probability=conn_prob | ||
| ) | ||
|
|
||
| """net.projections.append( | ||
| Projection( | ||
| id="proj1", | ||
| presynaptic=p0.id, | ||
| postsynaptic=p2.id, | ||
| synapse="gabaSyn", | ||
| delay=2, | ||
| weight=0.01, | ||
| ) | ||
| ) | ||
| net.projections[1].random_connectivity = RandomConnectivity(probability=1)""" | ||
|
|
||
| net.inputs.append( | ||
| Input( | ||
| id="stim", | ||
| input_source=input_source.id, | ||
| population=p0.id, | ||
| percentage=input_percentage, | ||
| weight=input_weight, | ||
| ) | ||
| ) | ||
|
|
||
| net_json_file = net.to_json_file("%s.json" % net.id) | ||
| net_yaml_file = net.to_yaml_file("%s.yaml" % net.id) | ||
|
|
||
| ################################################################################ | ||
| ### Build Simulation object & save as JSON | ||
|
|
||
| sim = Simulation( | ||
| id="Sim%s" % net.id, | ||
| network=net_json_file, | ||
| duration="1000", | ||
| dt="0.01", | ||
| record_traces={"all": "*"}, | ||
| record_spikes={"all": "*"} if "IF_" in pynn_cell else {}, | ||
| ) | ||
|
|
||
| sim.to_json_file() | ||
| sim.network = net_yaml_file | ||
| sim.to_yaml_file("%s.yaml" % sim.id) | ||
|
|
||
| sim.network = net_json_file # reverting, for call below... | ||
|
|
||
| return sim, net | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
|
|
||
| if "-one" in sys.argv: | ||
| sim, net = generate("OneCell", num_pop0=1, num_pop1=0, input_percentage=100) | ||
| elif "-hh" in sys.argv: | ||
| sim, net = generate( | ||
| "HH", num_pop0=1, num_pop1=0, input_percentage=0, pynn_cell="HH_cond_exp" | ||
| ) | ||
| elif "-input_weights" in sys.argv: | ||
| sim, net = generate( | ||
| "InputWeights", | ||
| num_pop0=4, | ||
| num_pop1=0, | ||
| input_percentage=62, | ||
| input_weight=".8*random()", | ||
| ) | ||
| elif "-simple_net" in sys.argv: | ||
| sim, net = generate( | ||
| "SimpleNet", | ||
| num_pop0=1, | ||
| num_pop1=1, | ||
| input_percentage=100, | ||
| input_weight="1", | ||
| ) | ||
| elif "-net1" in sys.argv: | ||
| sim, net = generate( | ||
| "Net1", | ||
| num_pop0=2, | ||
| num_pop1=3, | ||
| conn_weight="random()", | ||
| input_percentage=100, | ||
| input_weight="2*random()", | ||
| ) | ||
| else: | ||
| sim, net = generate("All") | ||
|
|
||
| ################################################################################ | ||
| ### Run in some simulators | ||
|
|
||
| from neuromllite.NetworkGenerator import check_to_generate_or_run | ||
|
|
||
| check_to_generate_or_run(sys.argv, sim) |
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